Application of smart robotics in the supply chain
bhargavpathri
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Sep 02, 2025
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About This Presentation
journal paper
Slide Content
Chapter 3
Applications of smart robotics in healthcare
3.1 Introduction
A smart or intelligent robot should be able to sense, move, and operate objects, components, tools, or specialized
equipment using interchangeable, planned movements. Artificial intelligence (AI) deals with the “brain function” or
thinking capability that a computer mainly performs (Dwivedi et al., 2022). Sensing something and manipulating or
repairing are “body functions” of a robot; they are based on computer science, mechanical, electrical, and electronic
engineering. Robotics and AI both have an impact on planning and task execution since they involve the brain and the
body, as shown in Fig. 3.1 (Padhan et al., 2023; Licardo et al., 2024).
Intelligent robots are transforming healthcare by means of better accuracy, efficiency, and patient outcomes derived
from improvements in medical service delivery (Wan et al., 2020). Combining surgical robots conducting challenging
operations with incredible precision with mobility robots allowing hospital logistical duties is changing healthcare
settings. Particularly after international health events like the coronavirus disease-2019 (COVID-19) outbreak, which
underscored the necessity of innovative healthcare solutions, sophisticated robotics helps to address the increasing need
for improved treatment (Okamura et al., 2010). Frequent applications for robotic systems in many medical disciplines,
including less-invasive surgery, rehabilitation, patient monitoring, and pharmaceutical delivery, include AI and machine
learning (ML) skills paired with sensors, data analytics, and actuators. These robots communicate with patients and
healthcare professionals, thus providing improved accuracy and real-time adaptation. While surgical robots enable
surgeons to execute difficult operations with more precision and reduced mistake rates, rehabilitation robots support
patients in recovery by tailoring therapeutic activities based on patient input (Guntur et al., 2019; Yang et al., 2020; Das
et al., 2024).
If smart robots are to be properly brought into medical systems, their development poses various difficulties that must
be solved. Obstacles to broader use include ethical questions about autonomy and responsibility, cybersecurity risks,
and challenges of societal acceptance and expenditures. Notwithstanding these obstacles, the advantages of robots,
including reduced demand on healthcare professionals, more accuracy in medical treatments, and tailored treatment, are
motivating their further development and deployment. This chapter addresses the development of smart robots in
healthcare, their many applications, and their future course of progress as well as it offers a comprehensive study of the
many kinds of robots already in use, their technical underpinnings, and the main possibilities and drawbacks these
machines provide (Ness et al., 2024). This chapter will discuss the necessary ethical, legal, and social factors for their
optimum functioning as well as evaluate real case studies displaying successful applications of smart robots. Finally, it
will provide opinions on future directions, underlining how AI, ML, and the digital twin are projected to challenge the
limits of smart robots in healthcare (Chen et al., 2015; Deo and Anjankar, 2023; Tripathi and Khondakar, 2024).
Q1
Q2
Bhargav Prajwal Pathri
*
Department of Mechatronics, Woxsen School of Technology, Hyderabad, Telangana, India[Instruction: In the
previous version there was another Author. Kindly Include the other author, Krishna Vamshi Ganduri, Assistant
Professor, School of Buisness, Woxsen University]
*
School of Technology, Woxsen University, Hyderabad, Telangana, India
i
The corrections made in this section will be reviewed and approved by the master copier.
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
Applications of smart robotics in healthcare
3.1 Introduction
A smart or intelligent robot should be able to sense, move, and operate objects, components, tools, or specialized
equipment using interchangeable, planned movements. Artificial intelligence (AI) deals with the “brain function” or
thinking capability that a computer mainly performs (Dwivedi et al., 2022). Sensing something and manipulating or
repairing are “body functions” of a robot; they are based on computer science, mechanical, electrical, and electronic
engineering. Robotics and AI both have an impact on planning and task execution since they involve the brain and the
body, as shown in Fig. 3.1 (Padhan et al., 2023; Licardo et al., 2024).
Intelligent robots are transforming healthcare by means of better accuracy, efficiency, and patient outcomes derived
from improvements in medical service delivery (Wan et al., 2020). Combining surgical robots conducting challenging
operations with incredible precision with mobility robots allowing hospital logistical duties is changing healthcare
settings. Particularly after international health events like the coronavirus disease-2019 (COVID-19) outbreak, which
underscored the necessity of innovative healthcare solutions, sophisticated robotics helps to address the increasing need
for improved treatment (Okamura et al., 2010). Frequent applications for robotic systems in many medical disciplines,
including less-invasive surgery, rehabilitation, patient monitoring, and pharmaceutical delivery, include AI and machine
learning (ML) skills paired with sensors, data analytics, and actuators. These robots communicate with patients and
healthcare professionals, thus providing improved accuracy and real-time adaptation. While surgical robots enable
surgeons to execute difficult operations with more precision and reduced mistake rates, rehabilitation robots support
patients in recovery by tailoring therapeutic activities based on patient input (Guntur et al., 2019; Yang et al., 2020; Das
et al., 2024).
If smart robots are to be properly brought into medical systems, their development poses various difficulties that must
be solved. Obstacles to broader use include ethical questions about autonomy and responsibility, cybersecurity risks,
and challenges of societal acceptance and expenditures. Notwithstanding these obstacles, the advantages of robots,
including reduced demand on healthcare professionals, more accuracy in medical treatments, and tailored treatment, are
motivating their further development and deployment. This chapter addresses the development of smart robots in
healthcare, their many applications, and their future course of progress as well as it offers a comprehensive study of the
many kinds of robots already in use, their technical underpinnings, and the main possibilities and drawbacks these
machines provide (Ness et al., 2024). This chapter will discuss the necessary ethical, legal, and social factors for their
optimum functioning as well as evaluate real case studies displaying successful applications of smart robots. Finally, it
will provide opinions on future directions, underlining how AI, ML, and the digital twin are projected to challenge the
limits of smart robots in healthcare (Chen et al., 2015; Deo and Anjankar, 2023; Tripathi and Khondakar, 2024).
Q1
Q2
Bhargav Prajwal Pathri
*
Department of Mechatronics, Woxsen School of Technology, Hyderabad, Telangana, India[Instruction: In the
previous version there was another Author. Kindly Include the other author, Krishna Vamshi Ganduri, Assistant
Professor, School of Buisness, Woxsen University]
*
School of Technology, Woxsen University, Hyderabad, Telangana, India
i
The corrections made in this section will be reviewed and approved by the master copier.
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
3.1.1 Overview of smart robotics in healthcare
In this era, robotics is rapidly evolving in healthcare field that leverages advanced technologies like Digital Twin (DT),
AI (Chatterjee et al., 2024), ML, deep learning (DL), and sensor integration to enhance healthcare surgeries, deliveries
and patient outcomes. Specifically, those robots are designed for performing tasks like surgeries and rehabilitation, to
handling administrative functions like patient health monitoring and medication dispensing. Since the need for human
physicians and nurses has exploded since the COVID-19 outbreak, more and more people are turning to intelligent
robots to assist with healthcare chores. Among the many smart robots used in the healthcare sector are surgical,
cooperative, social, microbots for focused medicine distribution, and rehabilitation robots. The healthcare ecosystem
enables each variation to be refined for different purposes (Shafik et al., 2024).
For example, in terms of enhancing the precision of minimally invasive procedures, the da Vinci Surgical System and
other surgical robots have progressed remarkably. These instruments help to increase surgical accuracy, therefore
reducing the chance of errors coming from human participation and hastening the healing process after surgical
procedures. Cooperative mobile robots are one area where progress is remarkable. These sorts of robots are supposed
to assist doctors in navigating hospital environments and doing administrative tasks. Their expertise covers medical
sample transfer between departments, prescription distribution, and cleaning of high-risk areas. This not only enhances
workflow but also decreases the physical load on the healthcare staff members are under, thereby helping intensive care
units (ICUs) and other much sought-after situations. The ability of intelligent robots to function either entirely or
partially independently has one main benefit: it helps to increase the efficacy of medical treatment. These self-sufficient
robots can gather new data, evaluate it, and apply what they have found right now thanks to AI and ML algorithms. AI
algorithms on robotic equipment allow them to provide diagnostics more quickly and accurately than more traditional
methods. This is accomplished by pointing out anomalies in X-rays and MRIs used in diagnosis (Kaur, 2024). Still,
challenges abound to overcome even with these advances. Among the extremely important ethical issues to weigh are
patient privacy, medical information confidentiality, and psychological readiness of physicians and patients to deal with
robotic intervention. Although a lot of studies have been done on healthcare robots, their general use in clinical
Figure 3.1
Alt-Text - Short Description: A diagram illustrating the integration of various healthcare technologies and services,
including telemedicine, wearable devices, AI and cloud computing.
Alt-Text - Long Description: The diagram illustrates the integration of various healthcare technologies and services. At the
center, there is a smartphone with icons representing healthcare services, communication and monitoring. Surrounding this
central image are various elements: a hospital building, a wearable device showing health metrics, a medical bed with
monitoring equipment, a house, a doctor on a video call, a cloud server and a hospital with an ambulance. These elements
are connected, indicating the interconnectedness of healthcare services through technology such as telemedicine, wearable
devices, AI and cloud computing.
Smart healthcare ecosystem. The next level of healthcare through advanced technology, where it explains through examples of smart
homes, drones, and wearables that would send patient data via a 5G network into an edge and cloud server to be analyzed and acted
on immediately. Through the implementation of AI, IoT, and cloud computing, such care will involve remote monitoring and
telemedicine with an assisted surgery through AI.
i
will be used in the final publication. Click on the image to view the original version.
Q3
In this era, robotics is rapidly evolving in healthcare field that leverages advanced technologies like Digital Twin (DT),
AI (Chatterjee et al., 2024), ML, deep learning (DL), and sensor integration to enhance healthcare surgeries, deliveries
and patient outcomes. Specifically, those robots are designed for performing tasks like surgeries and rehabilitation, to
handling administrative functions like patient health monitoring and medication dispensing. Since the need for human
physicians and nurses has exploded since the COVID-19 outbreak, more and more people are turning to intelligent
robots to assist with healthcare chores. Among the many smart robots used in the healthcare sector are surgical,
cooperative, social, microbots for focused medicine distribution, and rehabilitation robots. The healthcare ecosystem
enables each variation to be refined for different purposes (Shafik et al., 2024).
For example, in terms of enhancing the precision of minimally invasive procedures, the da Vinci Surgical System and
other surgical robots have progressed remarkably. These instruments help to increase surgical accuracy, therefore
reducing the chance of errors coming from human participation and hastening the healing process after surgical
procedures. Cooperative mobile robots are one area where progress is remarkable. These sorts of robots are supposed
to assist doctors in navigating hospital environments and doing administrative tasks. Their expertise covers medical
sample transfer between departments, prescription distribution, and cleaning of high-risk areas. This not only enhances
workflow but also decreases the physical load on the healthcare staff members are under, thereby helping intensive care
units (ICUs) and other much sought-after situations. The ability of intelligent robots to function either entirely or
partially independently has one main benefit: it helps to increase the efficacy of medical treatment. These self-sufficient
robots can gather new data, evaluate it, and apply what they have found right now thanks to AI and ML algorithms. AI
algorithms on robotic equipment allow them to provide diagnostics more quickly and accurately than more traditional
methods. This is accomplished by pointing out anomalies in X-rays and MRIs used in diagnosis (Kaur, 2024). Still,
challenges abound to overcome even with these advances. Among the extremely important ethical issues to weigh are
patient privacy, medical information confidentiality, and psychological readiness of physicians and patients to deal with
robotic intervention. Although a lot of studies have been done on healthcare robots, their general use in clinical
Figure 3.1
Alt-Text - Short Description: A diagram illustrating the integration of various healthcare technologies and services,
including telemedicine, wearable devices, AI and cloud computing.
Alt-Text - Long Description: The diagram illustrates the integration of various healthcare technologies and services. At the
center, there is a smartphone with icons representing healthcare services, communication and monitoring. Surrounding this
central image are various elements: a hospital building, a wearable device showing health metrics, a medical bed with
monitoring equipment, a house, a doctor on a video call, a cloud server and a hospital with an ambulance. These elements
are connected, indicating the interconnectedness of healthcare services through technology such as telemedicine, wearable
devices, AI and cloud computing.
Smart healthcare ecosystem. The next level of healthcare through advanced technology, where it explains through examples of smart
homes, drones, and wearables that would send patient data via a 5G network into an edge and cloud server to be analyzed and acted
on immediately. Through the implementation of AI, IoT, and cloud computing, such care will involve remote monitoring and
telemedicine with an assisted surgery through AI.
i
will be used in the final publication. Click on the image to view the original version.
Q3
situations has been restricted by factors like their high cost, need for maintenance, and complexity of their technological
design. Long term, however, robots offer great potential to overcome significant healthcare gaps like enhancing surgical
method precision, changing patient rehabilitation, and attending to senior patient demands (Denecke and Baudoin,
2022).
3.1.2 Historical perspective and evolution
The advent of robotic-assisted surgery in the 1980s was a momentous event that signaled a turning point in the
evolution of robotics into a medical application. The first well-known robotic system, PUMA 560, made it possible for
further advancements in robotic surgery when used for a neurosurgical biopsy in 1985. Made by Intuitive Surgical and
approved by the Food and Drug Administration in 2000, the most famous example of the continuous evolution of
laparoscopic robotic systems during the 1990s was the da Vinci Surgical System (Hirani et al., 2024). By allowing
surgeons to do challenging surgeries with better precision and hence shorten the duration of patient waiting for
recovery, this tool revolutionized minimally invasive surgery. Sensor technology (Afsana et al., 2018), ML, and AI
breakthroughs over the full 21st century helped to drive the development of healthcare robots. Using this innovative
technology produced autonomous robots are able to do tasks with little human help. One great example of this is the
CyberKnife robotic radiosurgery tool, developed in the late 1990s to remove tumors unable to remove manually.
CyberKnife presents a new degree of safety and accuracy to the area of cancer treatment by means of exact radiation
treatment to cancer patients and real-time modification of movement (Montero et al., 2024).
Medical professionals have increasingly been fascinated by cobots, often referred to as cooperative robots. These robots
have been designed to enable cooperation among human medical experts so that their work may be easier and more
efficient than it was in the past. Since they have been able to lower the risk of human-to-infectious disease transmission
by means of activities like equipment transfer, room cleaning, and medicine delivery, cobots have been shown to be an
indispensable tool in healthcare facilities. The COVID-19 epidemic really demonstrated this (Yang et al., 2023;
Karmakar et al., 2024). In the area of rehabilitation robots, there has also been a significant advancement at the turn of
the century. These robots are meant to help people recover from similar physical catastrophes, such as spinal injuries,
strokes, or other diseases. One of the ways these robotic therapists might be able to help patients undergoing motor
rehabilitation treatment more frequently than human therapists is by providing tailored, repetitive movement activities (
Mathkor et al., 2024; Soljacic et al., 2024). Using human movement patterns, the robotic gait trainer Lokomat helps
patients restore their capacity to walk while they are in rehabilitation. With the clear aim of helping people who are
elderly or have cognitive problems, along with other people, the field of healthcare robotics has developed into the
domain of social robots. By responding to touch and sound, therapeutic robots like Paro, which takes the form of a seal,
offer sufferers emotional support (Lalit et al., 2024; Bekbolatova et al., 2024). A very crucial component of the
therapeutic process, stress and anxiety may be helped to be reduced by this. In nursing homes and other long-term care
facilities, where humans are unable to be present constantly to provide ongoing care, these robots are very helpful.
Smart robots could find considerably more complex uses in the future. Among these uses are microbots, which are used
for the targeted delivery of certain pharmaceuticals, and digital twins, which are virtual representations of robotic
systems allowing predictive maintenance and real-time modelling of healthcare operations. These types of
developments show the growing role robots play in modern medicine (Kwon et al., 2022; Adel, 2024).
3.1.3 Objectives
Focusing on both current uses and future advances, this chapter attempts to investigate the growing relevance and
impact of smart robots in healthcare. The following represent the goals:
An investigation of many kinds of smart robots in healthcare, including surgical robots, mobility robots,
microbots, rehabilitation robots, and social robots, with an eye on their influence on medical procedures
and patient care.
1.
Examining the actual impact of smart robots in numerous medical settings, including surgery, ICUs,
rehabilitation, and drug distribution, emphasizes their applications.
2.
Presenting example cases stressing the advantages of the practicality as well as the challenges of
precisely deploying smart robots.
3.
Dealing with the ethical dilemmas, safety and privacy issues, societal acceptance, and cost-related 4.
design. Long term, however, robots offer great potential to overcome significant healthcare gaps like enhancing surgical
method precision, changing patient rehabilitation, and attending to senior patient demands (Denecke and Baudoin,
2022).
3.1.2 Historical perspective and evolution
The advent of robotic-assisted surgery in the 1980s was a momentous event that signaled a turning point in the
evolution of robotics into a medical application. The first well-known robotic system, PUMA 560, made it possible for
further advancements in robotic surgery when used for a neurosurgical biopsy in 1985. Made by Intuitive Surgical and
approved by the Food and Drug Administration in 2000, the most famous example of the continuous evolution of
laparoscopic robotic systems during the 1990s was the da Vinci Surgical System (Hirani et al., 2024). By allowing
surgeons to do challenging surgeries with better precision and hence shorten the duration of patient waiting for
recovery, this tool revolutionized minimally invasive surgery. Sensor technology (Afsana et al., 2018), ML, and AI
breakthroughs over the full 21st century helped to drive the development of healthcare robots. Using this innovative
technology produced autonomous robots are able to do tasks with little human help. One great example of this is the
CyberKnife robotic radiosurgery tool, developed in the late 1990s to remove tumors unable to remove manually.
CyberKnife presents a new degree of safety and accuracy to the area of cancer treatment by means of exact radiation
treatment to cancer patients and real-time modification of movement (Montero et al., 2024).
Medical professionals have increasingly been fascinated by cobots, often referred to as cooperative robots. These robots
have been designed to enable cooperation among human medical experts so that their work may be easier and more
efficient than it was in the past. Since they have been able to lower the risk of human-to-infectious disease transmission
by means of activities like equipment transfer, room cleaning, and medicine delivery, cobots have been shown to be an
indispensable tool in healthcare facilities. The COVID-19 epidemic really demonstrated this (Yang et al., 2023;
Karmakar et al., 2024). In the area of rehabilitation robots, there has also been a significant advancement at the turn of
the century. These robots are meant to help people recover from similar physical catastrophes, such as spinal injuries,
strokes, or other diseases. One of the ways these robotic therapists might be able to help patients undergoing motor
rehabilitation treatment more frequently than human therapists is by providing tailored, repetitive movement activities (
Mathkor et al., 2024; Soljacic et al., 2024). Using human movement patterns, the robotic gait trainer Lokomat helps
patients restore their capacity to walk while they are in rehabilitation. With the clear aim of helping people who are
elderly or have cognitive problems, along with other people, the field of healthcare robotics has developed into the
domain of social robots. By responding to touch and sound, therapeutic robots like Paro, which takes the form of a seal,
offer sufferers emotional support (Lalit et al., 2024; Bekbolatova et al., 2024). A very crucial component of the
therapeutic process, stress and anxiety may be helped to be reduced by this. In nursing homes and other long-term care
facilities, where humans are unable to be present constantly to provide ongoing care, these robots are very helpful.
Smart robots could find considerably more complex uses in the future. Among these uses are microbots, which are used
for the targeted delivery of certain pharmaceuticals, and digital twins, which are virtual representations of robotic
systems allowing predictive maintenance and real-time modelling of healthcare operations. These types of
developments show the growing role robots play in modern medicine (Kwon et al., 2022; Adel, 2024).
3.1.3 Objectives
Focusing on both current uses and future advances, this chapter attempts to investigate the growing relevance and
impact of smart robots in healthcare. The following represent the goals:
An investigation of many kinds of smart robots in healthcare, including surgical robots, mobility robots,
microbots, rehabilitation robots, and social robots, with an eye on their influence on medical procedures
and patient care.
1.
Examining the actual impact of smart robots in numerous medical settings, including surgery, ICUs,
rehabilitation, and drug distribution, emphasizes their applications.
2.
Presenting example cases stressing the advantages of the practicality as well as the challenges of
precisely deploying smart robots.
3.
Dealing with the ethical dilemmas, safety and privacy issues, societal acceptance, and cost-related 4.
3.1.4 Contributions
3.2 Literature review
The COVID-19 pandemic certainly ranks among the greatest global catastrophes of the 21st century, second only to
significant historical events such as World War II in its incidence. In just a short period, this virus rapidly swept across
the globe, making populations around the world employ extreme preventive measures that drastically changed the face
of healthcare systems. Therefore, pressure on the health sector in terms of supplying enormous medical equipment and
innovative solutions for treatments at unprecedented levels became paramount. This led to the adaptation of such
advanced technologies as AI and robots, which have tremendous potential in a variety of applications related to
pandemic diagnosis, risk assessment, monitoring, telemedicine, disinfection, and numerous other healthcare operations.
These technologies considerably reduced the burden on front-line health workers (Sarker et al., 2021).
AI tools played a highly instrumental role in accelerating the development of COVID-19 vaccines, and robotics
platforms were integral in expediting an effective vaccine distribution process. Apart from these benefits in terms of
logistics, AI and robotic technologies also played a role in solving mental health problems linked to the effects of the
pandemic, mainly by automation and personal care. Several studies have researched the use of AI and robotics in the
pandemic. Researchers used the PRISMA method in reviewing 147 records with a focus on the applications of AI and
robotics in healthcare during the COVID-19 crisis (Hussain et al., 2020). This work exemplifies how these
technologies change healthcare delivery through better service and patient care. However, the utilization of such
technologies carries several data privacy and security challenges. A primary source of anxiety is in integrating ML
models into the healthcare environment, especially as it pertains to EHRs. The reason is primarily because each medical
server has its own training data, and so the general AI model across various systems cannot be achieved. This further
complicates the process because health data is sensitive and needs proper labelling, proper handling of patient
information, and continued prevention of false positives in intrusion detection systems (IDS; Khang, 2024).
The IoT has also significantly contributed to tracking and controlling the spread of the pandemic (Keshary et al., 2022).
There are several IoT-based frameworks that aid in tracing COVID-19 patients, predicting the effectiveness of
treatments, and evaluating the impact of various interventions. For instance, IoT systems and AI algorithms have been
used in the process of analyzing patient data (Ahmed et al., 2023). This has given profound insights into how
treatments might be working, and it has also made possible the monitoring of patient health parameters in real-time (
Acharya and Patil, 2020). The system, combined with other technologies like thermal imaging and facial recognition,
has played a vital role in controlling the virus. Emerging technologies, like 5G, drones, and blockchain, have been
investigated in terms of their utility in combating the pandemic (Siriwardhana et al., 2020). Use cases for deployment
have been explored in supply chain management, remote monitoring, and data security. Innovative solutions from
challenges surrounding the use of smart robots in healthcare.
Presenting future directions include the ideas of a digital twin to foresee the evolution of robots in
healthcare, AI integration, and autonomous and assistive system roles.
5.
It offers a comprehensive summary of many intelligent robots and their applications in the medical field.1.
By means of sensor, actuator, and data processing deconstruction. The chapter outlines the technical
underpinnings of medical uses and smart robots.
2.
The chapter discusses smart robots in operating rooms and rehabilitation clinics using pragmatic
applications.
3.
Case studies highlight how clever robots are transforming medical treatments and stress-reducing
strategies gained upon deployment.
4.
Juggling opportunities and challenges, it looks closely at ethical, security, societal, and economic
barriers to the smart robot acceptance.
5.
The chapter addresses future concerns such as AI-robotic integration, human-robot cooperation, and
digital twin technologies to guide healthcare robotics research and development.
6.
3.2 Literature review
The COVID-19 pandemic certainly ranks among the greatest global catastrophes of the 21st century, second only to
significant historical events such as World War II in its incidence. In just a short period, this virus rapidly swept across
the globe, making populations around the world employ extreme preventive measures that drastically changed the face
of healthcare systems. Therefore, pressure on the health sector in terms of supplying enormous medical equipment and
innovative solutions for treatments at unprecedented levels became paramount. This led to the adaptation of such
advanced technologies as AI and robots, which have tremendous potential in a variety of applications related to
pandemic diagnosis, risk assessment, monitoring, telemedicine, disinfection, and numerous other healthcare operations.
These technologies considerably reduced the burden on front-line health workers (Sarker et al., 2021).
AI tools played a highly instrumental role in accelerating the development of COVID-19 vaccines, and robotics
platforms were integral in expediting an effective vaccine distribution process. Apart from these benefits in terms of
logistics, AI and robotic technologies also played a role in solving mental health problems linked to the effects of the
pandemic, mainly by automation and personal care. Several studies have researched the use of AI and robotics in the
pandemic. Researchers used the PRISMA method in reviewing 147 records with a focus on the applications of AI and
robotics in healthcare during the COVID-19 crisis (Hussain et al., 2020). This work exemplifies how these
technologies change healthcare delivery through better service and patient care. However, the utilization of such
technologies carries several data privacy and security challenges. A primary source of anxiety is in integrating ML
models into the healthcare environment, especially as it pertains to EHRs. The reason is primarily because each medical
server has its own training data, and so the general AI model across various systems cannot be achieved. This further
complicates the process because health data is sensitive and needs proper labelling, proper handling of patient
information, and continued prevention of false positives in intrusion detection systems (IDS; Khang, 2024).
The IoT has also significantly contributed to tracking and controlling the spread of the pandemic (Keshary et al., 2022).
There are several IoT-based frameworks that aid in tracing COVID-19 patients, predicting the effectiveness of
treatments, and evaluating the impact of various interventions. For instance, IoT systems and AI algorithms have been
used in the process of analyzing patient data (Ahmed et al., 2023). This has given profound insights into how
treatments might be working, and it has also made possible the monitoring of patient health parameters in real-time (
Acharya and Patil, 2020). The system, combined with other technologies like thermal imaging and facial recognition,
has played a vital role in controlling the virus. Emerging technologies, like 5G, drones, and blockchain, have been
investigated in terms of their utility in combating the pandemic (Siriwardhana et al., 2020). Use cases for deployment
have been explored in supply chain management, remote monitoring, and data security. Innovative solutions from
challenges surrounding the use of smart robots in healthcare.
Presenting future directions include the ideas of a digital twin to foresee the evolution of robots in
healthcare, AI integration, and autonomous and assistive system roles.
5.
It offers a comprehensive summary of many intelligent robots and their applications in the medical field.1.
By means of sensor, actuator, and data processing deconstruction. The chapter outlines the technical
underpinnings of medical uses and smart robots.
2.
The chapter discusses smart robots in operating rooms and rehabilitation clinics using pragmatic
applications.
3.
Case studies highlight how clever robots are transforming medical treatments and stress-reducing
strategies gained upon deployment.
4.
Juggling opportunities and challenges, it looks closely at ethical, security, societal, and economic
barriers to the smart robot acceptance.
5.
The chapter addresses future concerns such as AI-robotic integration, human-robot cooperation, and
digital twin technologies to guide healthcare robotics research and development.
6.
advanced technologies, such as AI, IoT, and others, have shown value to healthcare in mitigating the challenges of
COVID-19. Thereby, there are various issues at play (Ammae et al., 2018; James et al., 2024). In terms of data security,
connectivity issues can cause interoperability problems and even ethical considerations against the spread of AI and
robotics in the healthcare sector. However, much research is going on in these respective fields, and it's expected that
these problems can be bettered to bring into force more robust solutions that are more scalable soon (Santos et al., 2021;
Siripurapu et al., 2023).
3.3 Methodology
The roadmap of the smart robot health care system is depicted in Fig. 3.2.
3.4 Types of smart robots in healthcare
Smart robotics in healthcare is a vast and diverse field, with different types of robots designed to fulfill specialized roles
in clinical environments. These robotic systems harness advancements in AI, ML, and sensor technologies to assist
healthcare professionals, enhance precision, and deliver improved patient care. The following are some key types of
smart robots used in healthcare:
3.4.1 Surgical robots
Surgical robots, such as the Mako Robotic-Arm and the da Vinci Surgical System, improve stability, dexterity, and
precision during minimally invasive practices (Ngu et al., 2024). Frequently used in urology, gynecology, and
cardiology operations, the da Vinci system provides three-dimensional imagery and robotic arms that mimic surgeon
actions, so improving accuracy and reducing complications. Mostly used in orthopedic cases, Mako uses 3D models to
precisely position implants in joint replacements. CyberKnife also targets malignancies, notably in radiation treatment,
therefore minimizing damage to surrounding healthy tissue and improving general patient outcomes (Biswas et al.,
2023; Fig. 3.3).
Figure 3.2
Alt-Text - Short Description: Flowchart depicting a sequence of steps with icons representing different stages.
Alt-Text - Long Description: The image shows a horizontal flowchart with seven steps connected by arrows. Each step is
represented by a hexagonal platform with an icon on top. The icons, from left to right, include a person with a checklist, a
person with documents, a bug, a person with a magnifying glass, a target with a dart, a person with a car and a clipboard
with a document. Each step is linked by arrows indicating the flow from one step to the next.
Road map. Roadmap of the smart robot health care system is depicted.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Figure 3.3
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
COVID-19. Thereby, there are various issues at play (Ammae et al., 2018; James et al., 2024). In terms of data security,
connectivity issues can cause interoperability problems and even ethical considerations against the spread of AI and
robotics in the healthcare sector. However, much research is going on in these respective fields, and it's expected that
these problems can be bettered to bring into force more robust solutions that are more scalable soon (Santos et al., 2021;
Siripurapu et al., 2023).
3.3 Methodology
The roadmap of the smart robot health care system is depicted in Fig. 3.2.
3.4 Types of smart robots in healthcare
Smart robotics in healthcare is a vast and diverse field, with different types of robots designed to fulfill specialized roles
in clinical environments. These robotic systems harness advancements in AI, ML, and sensor technologies to assist
healthcare professionals, enhance precision, and deliver improved patient care. The following are some key types of
smart robots used in healthcare:
3.4.1 Surgical robots
Surgical robots, such as the Mako Robotic-Arm and the da Vinci Surgical System, improve stability, dexterity, and
precision during minimally invasive practices (Ngu et al., 2024). Frequently used in urology, gynecology, and
cardiology operations, the da Vinci system provides three-dimensional imagery and robotic arms that mimic surgeon
actions, so improving accuracy and reducing complications. Mostly used in orthopedic cases, Mako uses 3D models to
precisely position implants in joint replacements. CyberKnife also targets malignancies, notably in radiation treatment,
therefore minimizing damage to surrounding healthy tissue and improving general patient outcomes (Biswas et al.,
2023; Fig. 3.3).
Figure 3.2
Alt-Text - Short Description: Flowchart depicting a sequence of steps with icons representing different stages.
Alt-Text - Long Description: The image shows a horizontal flowchart with seven steps connected by arrows. Each step is
represented by a hexagonal platform with an icon on top. The icons, from left to right, include a person with a checklist, a
person with documents, a bug, a person with a magnifying glass, a target with a dart, a person with a car and a clipboard
with a document. Each step is linked by arrows indicating the flow from one step to the next.
Road map. Roadmap of the smart robot health care system is depicted.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Figure 3.3
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Thanks to Mako, a surgical robot known for precision, 66-year-old Alex, with stage-4 arthritis, had knee replacement
surgery in India. Using CT pictures, Mako created a 3D model of Alex's knee that allowed the surgeon to make exact
bone cuts and reduce tissue damage, therefore encouraging fast healing and a decrease in discomfort. Because of its
better accuracy and outcomes, about 75% of knee replacement patients now choose robotic surgery (Yang et al., 2022;
Knudsen et al., 2024). Apart from standard surgical operations like hip replacements, more difficult operations
involving the heart, lungs, and cancer are adopting robotic systems like Mako and Da Vinci. The enhanced three-
dimensional pictures of internal organs and 360-degree wrist movement of these robots provide amazing accuracy and
control, which surgeons cherish. Robotic surgeries produce faster healing times, less bleeding, and less postoperative
pain which lets patients start routine activities one to 2 weeks after surgery (Kim et al., 2024). More and more doctors
and patients all over are using this technology as it can improve surgery outcomes in many different fields, as shown in
Fig. 3.3.
3.4.2 Mobile robots
Among the numerous tasks mobile robots are doing in the medical field are patient monitoring, medication dispensing,
and professional assistance. These robots can move either on their own or under very limited human control thanks to
their sensors, CPUs, and actuators. As they find novel uses in healthcare, mobile robots are replacing jobs that people
find too unsafe, tedious, or boring (Fig. 3.4).
Alt-Text - Short Description: Illustrations and images of a surgical procedure and robotic surgery setup.
Alt-Text - Long Description: The first illustration shows two medical professionals, one standing and one sitting, in an
operating room with surgical lights and equipment. The second image shows a surgeon performing a procedure on a patient.
The third illustration depicts a doctor standing next to a controller with a screen displaying medical images. The fourth
image shows a surgery robot setup with a table and a screen displaying medical images.
Traditional and robot-assisted surgery: robot operating interventional surgery. A surgeon can remotely control a robot via an
interface. The doctor watches the robot execute precise, less intrusive surgeries from a control station. Surgeon radiation exposure is
reduced and human expertise and robotic accuracy are combined for best results.
Q4
Figure 3.4
Alt-Text - Short Description: Composite showcasing various models of robots designed for different functions in healthcare
and industrial settings.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
surgery in India. Using CT pictures, Mako created a 3D model of Alex's knee that allowed the surgeon to make exact
bone cuts and reduce tissue damage, therefore encouraging fast healing and a decrease in discomfort. Because of its
better accuracy and outcomes, about 75% of knee replacement patients now choose robotic surgery (Yang et al., 2022;
Knudsen et al., 2024). Apart from standard surgical operations like hip replacements, more difficult operations
involving the heart, lungs, and cancer are adopting robotic systems like Mako and Da Vinci. The enhanced three-
dimensional pictures of internal organs and 360-degree wrist movement of these robots provide amazing accuracy and
control, which surgeons cherish. Robotic surgeries produce faster healing times, less bleeding, and less postoperative
pain which lets patients start routine activities one to 2 weeks after surgery (Kim et al., 2024). More and more doctors
and patients all over are using this technology as it can improve surgery outcomes in many different fields, as shown in
Fig. 3.3.
3.4.2 Mobile robots
Among the numerous tasks mobile robots are doing in the medical field are patient monitoring, medication dispensing,
and professional assistance. These robots can move either on their own or under very limited human control thanks to
their sensors, CPUs, and actuators. As they find novel uses in healthcare, mobile robots are replacing jobs that people
find too unsafe, tedious, or boring (Fig. 3.4).
Alt-Text - Short Description: Illustrations and images of a surgical procedure and robotic surgery setup.
Alt-Text - Long Description: The first illustration shows two medical professionals, one standing and one sitting, in an
operating room with surgical lights and equipment. The second image shows a surgeon performing a procedure on a patient.
The third illustration depicts a doctor standing next to a controller with a screen displaying medical images. The fourth
image shows a surgery robot setup with a table and a screen displaying medical images.
Traditional and robot-assisted surgery: robot operating interventional surgery. A surgeon can remotely control a robot via an
interface. The doctor watches the robot execute precise, less intrusive surgeries from a control station. Surgeon radiation exposure is
reduced and human expertise and robotic accuracy are combined for best results.
Q4
Figure 3.4
Alt-Text - Short Description: Composite showcasing various models of robots designed for different functions in healthcare
and industrial settings.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Inspired originally by military surveillance, search and rescue, and industrial automation, these sectors also include
Mobile robots’ range in scale and design, from small, light ones ideal for indoor usage to large, heavy-duty robots able
to handle challenging terrain. By use of AI, ML, and computer vision, these robots perceive their surroundings,
develop action plans, and independently do tasks. Mobile robots should grow in significance in healthcare since they,
via means of betterment of technology, serve to increase efficiency and patient outcomes (Kondaveti et al., 2019).
Among other uses, mobile robots are seen in hospitals for cleaning, patient monitoring, and pharmaceutical delivery.
For example, by cleaning even the most far-off areas, UV-C (UV-C) lamp-equipped robots may disinfect rooms and
lower the risk of illnesses connected to healthcare. In the framework of assisted living, mobile robots provide
customized care to the elderly and those with physical or cognitive restrictions by supporting cleaning, task reminders,
and mobility aids. These robots not only encourage autonomy but also improve security by informing medical
personnel of any problems. Hospitals also deploy mobile robots such as Yumi, Saul robot, TUG, and Moxi for
logistical chores like supply, food, and drug delivery as shown in Fig. 3.4. This helps to lighten part of the strain on
medical personnel and reduces the possibility of human mistakes. For patient care enhancement, cost management, and
efficiency of healthcare systems, mobile robots offer a reasonable option (Bernhard et al., 2024; Kebede et al., 2024).
3.4.3 Microbots
Microbots, also known as nanobots or medical microrobots, represent a breakthrough in targeted therapy and minimally
invasive medical treatments, as shown in Fig. 3.5 (Agrawal et al., 2024). These tiny robots, often measured in
micrometers, are designed to navigate the human body to deliver drugs directly to diseased tissues or cells. Microbots
are particularly useful in treating conditions like cancer, where traditional treatments such as chemotherapy affect both
healthy and cancerous cells, causing harmful side effects (Jia et al., 2024; Moshayedi et al., 2024). Microbots are
guided through the body using various propulsion mechanisms, including electromagnetic fields or biological forces
like blood flow. They can be designed to respond to specific biological signals, such as changes in pH levels or
temperature, to release drugs precisely where they are needed. For instance, Thera grippers are micro-sized robots that
attach themselves to the intestinal walls and release medication gradually, improving the efficacy of drug delivery
systems. One of the most promising future applications of microbots is in minimally invasive surgeries, where these tiny
robots could perform tasks like clearing blocked arteries or delivering targeted radiation directly to tumors. Despite the
challenges in deploying microbots at scale, their potential to revolutionize drug delivery and diagnostics is immense (
Shah et al., 2022).
Alt-Text - Long Description: The image displays a collection of ten different robots, each designed for specific functions,
primarily in healthcare and industrial environments. The robots vary in design, from humanoid forms to more utilitarian,
cylindrical shapes. Some robots are equipped with screens, others with articulated arms or disinfection systems. Each robot is
depicted either in a clinical setting, like a hospital corridor, or against a neutral background. The robots are shown
performing tasks such as navigating hallways, interacting with human operators, or standing idle.
Mobile robots: various mobile robots used in healthcare applications.
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
Mobile robots’ range in scale and design, from small, light ones ideal for indoor usage to large, heavy-duty robots able
to handle challenging terrain. By use of AI, ML, and computer vision, these robots perceive their surroundings,
develop action plans, and independently do tasks. Mobile robots should grow in significance in healthcare since they,
via means of betterment of technology, serve to increase efficiency and patient outcomes (Kondaveti et al., 2019).
Among other uses, mobile robots are seen in hospitals for cleaning, patient monitoring, and pharmaceutical delivery.
For example, by cleaning even the most far-off areas, UV-C (UV-C) lamp-equipped robots may disinfect rooms and
lower the risk of illnesses connected to healthcare. In the framework of assisted living, mobile robots provide
customized care to the elderly and those with physical or cognitive restrictions by supporting cleaning, task reminders,
and mobility aids. These robots not only encourage autonomy but also improve security by informing medical
personnel of any problems. Hospitals also deploy mobile robots such as Yumi, Saul robot, TUG, and Moxi for
logistical chores like supply, food, and drug delivery as shown in Fig. 3.4. This helps to lighten part of the strain on
medical personnel and reduces the possibility of human mistakes. For patient care enhancement, cost management, and
efficiency of healthcare systems, mobile robots offer a reasonable option (Bernhard et al., 2024; Kebede et al., 2024).
3.4.3 Microbots
Microbots, also known as nanobots or medical microrobots, represent a breakthrough in targeted therapy and minimally
invasive medical treatments, as shown in Fig. 3.5 (Agrawal et al., 2024). These tiny robots, often measured in
micrometers, are designed to navigate the human body to deliver drugs directly to diseased tissues or cells. Microbots
are particularly useful in treating conditions like cancer, where traditional treatments such as chemotherapy affect both
healthy and cancerous cells, causing harmful side effects (Jia et al., 2024; Moshayedi et al., 2024). Microbots are
guided through the body using various propulsion mechanisms, including electromagnetic fields or biological forces
like blood flow. They can be designed to respond to specific biological signals, such as changes in pH levels or
temperature, to release drugs precisely where they are needed. For instance, Thera grippers are micro-sized robots that
attach themselves to the intestinal walls and release medication gradually, improving the efficacy of drug delivery
systems. One of the most promising future applications of microbots is in minimally invasive surgeries, where these tiny
robots could perform tasks like clearing blocked arteries or delivering targeted radiation directly to tumors. Despite the
challenges in deploying microbots at scale, their potential to revolutionize drug delivery and diagnostics is immense (
Shah et al., 2022).
Alt-Text - Long Description: The image displays a collection of ten different robots, each designed for specific functions,
primarily in healthcare and industrial environments. The robots vary in design, from humanoid forms to more utilitarian,
cylindrical shapes. Some robots are equipped with screens, others with articulated arms or disinfection systems. Each robot is
depicted either in a clinical setting, like a hospital corridor, or against a neutral background. The robots are shown
performing tasks such as navigating hallways, interacting with human operators, or standing idle.
Mobile robots: various mobile robots used in healthcare applications.
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
3.4.4 Rehabilitation robots
Rehabilitation robots can help patients undergoing physical therapy or rehabilitation following their injuries, surgical
operations, or neurological diseases. Those who are paralyzed or otherwise unable to move freely owing to diseases
like strokes or spinal cord injuries would benefit tremendously from the use of robots like this one. Sensors and AI
algorithms in rehabilitative robots enable tracking of the patient's development as shown in Fig. 3.6 (Krebs and Volpe,
2015). These robots also real-time adjust therapy regimens to fulfill specific needs. Among the most creative tools
available for rehabilitation nowadays is the Lokomat robotic exoskeleton. By directing their legs through a series of
consistent walking motions, it aids in the patient's recovery of the capacity to walk. By use of a treadmill system and a
harness bearing the patient's weight, Lokomat may imitate the sense of walking. Given this, stroke patients might be
able to start gait training sooner, which is important for their neuroplasticity and motor recovery. Other robots, like
Armeo from Hocoma, are designed mainly for upper limb rehabilitation. During rehabilitation exercises, Armeo
provides patients with both resistance and encouragement so they may regain arm movement after a stroke or other
damage. The device may modify therapy plans to provide the most appropriate outcomes for the patient's recovery by
means of real-time data received from the motions of the patient. More consistent, intensive, and tailored therapy
treatments that rehabilitation robots may provide lead to faster recovery timeframes and improved functional results (
Gardner et al., 2017).
Figure 3.5
Alt-Text - Short Description: An infographic showing different types of microrobots and their applications.
Alt-Text - Long Description: The infographic is titled 'Microrobots' and is divided into four sections, each describing
different types of microrobots and their applications. The sections are as follows: Magneto-catalytic: Fe O-NPs, Magnetic,
Optic-catalytic. Magnetic Janus microbot, Magnetic Porous microrobot, Mn O sub 2 end sub - micromotor. RNA-biomineral
nanorobot, Magnetic microrobot with a titanate surface, Magnetic 3D-printed double-helical microrobot. Magnetic microbot
with NIR, Nitric oxide nanometre loaded in microneedles, Magnetic helical micrometre.
Micro bots. All these nano-robots are and millimeter-sized and these robots are highly precise machines to be used in the human body
for different purposes: (1) Drug delivery: Microrobots can deliver drugs directly to target cells, improving efficacy and reducing side
effects. (2) Cell manipulation: They can manipulate cells such as the stem cells for regenerative medicine and tissue engineering. (3)
Invasive surgeries: therefore, in minimally, microrobots can be used as a source of accurate and controlled support. (4) Biosensing:
They are used as sensors to recognize biomarkers and monitor how the disease is progressing.
i
will be used in the final publication. Click on the image to view the original version.
Figure 3.6
Alt-Text - Short Description: A collage presents individuals using different types of rehabilitation technology.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Rehabilitation robots can help patients undergoing physical therapy or rehabilitation following their injuries, surgical
operations, or neurological diseases. Those who are paralyzed or otherwise unable to move freely owing to diseases
like strokes or spinal cord injuries would benefit tremendously from the use of robots like this one. Sensors and AI
algorithms in rehabilitative robots enable tracking of the patient's development as shown in Fig. 3.6 (Krebs and Volpe,
2015). These robots also real-time adjust therapy regimens to fulfill specific needs. Among the most creative tools
available for rehabilitation nowadays is the Lokomat robotic exoskeleton. By directing their legs through a series of
consistent walking motions, it aids in the patient's recovery of the capacity to walk. By use of a treadmill system and a
harness bearing the patient's weight, Lokomat may imitate the sense of walking. Given this, stroke patients might be
able to start gait training sooner, which is important for their neuroplasticity and motor recovery. Other robots, like
Armeo from Hocoma, are designed mainly for upper limb rehabilitation. During rehabilitation exercises, Armeo
provides patients with both resistance and encouragement so they may regain arm movement after a stroke or other
damage. The device may modify therapy plans to provide the most appropriate outcomes for the patient's recovery by
means of real-time data received from the motions of the patient. More consistent, intensive, and tailored therapy
treatments that rehabilitation robots may provide lead to faster recovery timeframes and improved functional results (
Gardner et al., 2017).
Figure 3.5
Alt-Text - Short Description: An infographic showing different types of microrobots and their applications.
Alt-Text - Long Description: The infographic is titled 'Microrobots' and is divided into four sections, each describing
different types of microrobots and their applications. The sections are as follows: Magneto-catalytic: Fe O-NPs, Magnetic,
Optic-catalytic. Magnetic Janus microbot, Magnetic Porous microrobot, Mn O sub 2 end sub - micromotor. RNA-biomineral
nanorobot, Magnetic microrobot with a titanate surface, Magnetic 3D-printed double-helical microrobot. Magnetic microbot
with NIR, Nitric oxide nanometre loaded in microneedles, Magnetic helical micrometre.
Micro bots. All these nano-robots are and millimeter-sized and these robots are highly precise machines to be used in the human body
for different purposes: (1) Drug delivery: Microrobots can deliver drugs directly to target cells, improving efficacy and reducing side
effects. (2) Cell manipulation: They can manipulate cells such as the stem cells for regenerative medicine and tissue engineering. (3)
Invasive surgeries: therefore, in minimally, microrobots can be used as a source of accurate and controlled support. (4) Biosensing:
They are used as sensors to recognize biomarkers and monitor how the disease is progressing.
i
will be used in the final publication. Click on the image to view the original version.
Figure 3.6
Alt-Text - Short Description: A collage presents individuals using different types of rehabilitation technology.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
3.4.5 Social robots
Social robots allow medical personnel and patients to engage in social or emotional capacity interactions. Made to
interact with people, these robots are equipped with sensors and strong AI algorithms. These robots can remind patients
to choose their prescriptions, continue conversations with them, and even provide emotional support. Apart from elder
care, social robots particularly assist in cognitive treatment for those with autism or dementia shown in Fig. 3.7 (Rasouli
et al., 2022). Well-known in the medical sector as a social robot is Paro, a robotic therapeutic seal. Patients in long-term
care facilities can benefit from their abilities to respond to touch, sound, and light as it replicates the behavior of a real
animal and gives company to such people. Particularly for individuals who are either socially isolated or have limited
opportunities for social connection, this participation helps patients release stress and anxiety (Fig. 3.7).
Alt-Text - Long Description: The image is composed of three separate circular frames. In the first frame, a healthcare
professional is assisting a patient with a robotic arm device attached to the patient's arm. In the second frame, an individual is
seated at a rehabilitation device, interacting with a virtual reality game displayed on a screen, guided by another person. In
the third frame, a person is using a walking assistance device, supported by two healthcare professionals.
Rehabilitation robot. Rehabilitation robots can help patients undergoing physiocal therapy or rehabilitation. Three types of
healthcare rehabilitation robots exist. The Kuka, Armeo, and Re-walk exoskeletons help upper and lower limbs. These tools inspire
patients, provide focused therapy, and measure objective results to hasten recovery and improve function.
Figure 3.7
Alt-Text - Short Description: A collage eaturing various robots and one plush toy.
Alt-Text - Long Description: The image displays a collection of seven different robots and one plush toy. The robots vary
in design and complexity. The first robot is humanoid with a white and black body. The second robot is also humanoid,
featuring a display screen on its chest showing a heart symbol. The third robot has a vintage design with a screen displaying
text and a cylindrical body. The fourth robot is humanoid with a sleek, modern design. The fifth image is a plush toy
resembling a seal. The sixth robot is a small, dog-like robot with a streamlined design. The seventh robot is humanoid,
sitting down with a robust and mechanical appearance.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Social robots allow medical personnel and patients to engage in social or emotional capacity interactions. Made to
interact with people, these robots are equipped with sensors and strong AI algorithms. These robots can remind patients
to choose their prescriptions, continue conversations with them, and even provide emotional support. Apart from elder
care, social robots particularly assist in cognitive treatment for those with autism or dementia shown in Fig. 3.7 (Rasouli
et al., 2022). Well-known in the medical sector as a social robot is Paro, a robotic therapeutic seal. Patients in long-term
care facilities can benefit from their abilities to respond to touch, sound, and light as it replicates the behavior of a real
animal and gives company to such people. Particularly for individuals who are either socially isolated or have limited
opportunities for social connection, this participation helps patients release stress and anxiety (Fig. 3.7).
Alt-Text - Long Description: The image is composed of three separate circular frames. In the first frame, a healthcare
professional is assisting a patient with a robotic arm device attached to the patient's arm. In the second frame, an individual is
seated at a rehabilitation device, interacting with a virtual reality game displayed on a screen, guided by another person. In
the third frame, a person is using a walking assistance device, supported by two healthcare professionals.
Rehabilitation robot. Rehabilitation robots can help patients undergoing physiocal therapy or rehabilitation. Three types of
healthcare rehabilitation robots exist. The Kuka, Armeo, and Re-walk exoskeletons help upper and lower limbs. These tools inspire
patients, provide focused therapy, and measure objective results to hasten recovery and improve function.
Figure 3.7
Alt-Text - Short Description: A collage eaturing various robots and one plush toy.
Alt-Text - Long Description: The image displays a collection of seven different robots and one plush toy. The robots vary
in design and complexity. The first robot is humanoid with a white and black body. The second robot is also humanoid,
featuring a display screen on its chest showing a heart symbol. The third robot has a vintage design with a screen displaying
text and a cylindrical body. The fourth robot is humanoid with a sleek, modern design. The fifth image is a plush toy
resembling a seal. The sixth robot is a small, dog-like robot with a streamlined design. The seventh robot is humanoid,
sitting down with a robust and mechanical appearance.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Pepper is another social robot capable of continuing conversations, identifying human emotions, and providing useful
help based on pragmatic need. Patients could be reminded, for example, of following their advice or picking up their
prescription, or of completing rehabilitation programs. In the future, social robots might become more and more crucial
in providing companionship and helping to solve mental health problems. Given the expected ongoing increase in elder
care demand, this is especially true (Tavakoli et al., 2020). A gem designed to assist patients with daily chores such as
collecting objects or traversing their surroundings, this little wheeled robot. Like Pearl, the mobile robot known as
Myon can do a variety of functions, including drug distribution and reminder provision. Designed especially for people
with autism or dementia, Aibo is a robotic dog meant for use as a friend or therapeutic aid. Robear is a humanoid robot
meant for use in patient care. It can enable patients to move about so they can improve or raise their level of treatment (
Tulsulkar et al., 2021; Silvera-Tawil, 2024).
3.5 Technological components of smart robotics
“Smart robots” are a convergence of several creative technologies within the healthcare industry that enable them to
either entirely or partially execute difficult tasks independently. The fundamental elements of these systems are
actuators, data processing units, and sensors. Moreover, when working together, they enable robots to investigate their
surroundings, make fast judgments, and carry out specific duties. These are the fundamental technological parts
enabling the intelligent robot to run in medical surroundings.
3.5.1 IOT-aided robot technology in healthcare
The third most Internet of Things (IoT) embracing industry worldwide is healthcare. This shows its crucial relevance
for better operational effectiveness and patient outcomes. The real-time data exchange made possible by the Internet of
Things (IoT) helps doctors to evaluate a patient's situation. Improved decisions, less needless hospital visits, and
improved general health care may all be achieved with this technology, helping individuals. Automated record keeping
and data sharing also help to increase healthcare efficiency by means of teamwork among different care teams shown in
Fig. 3.8 (Pradhan et al., 2021). The image shows some of the most important uses of mobile robots in medical
environments. These people have duties in cleanup, food and medicine distribution, behavior monitoring, including
mask wearing and social distancing, and hospital running help. These robots also help to treat heart disease, enforce
lockdowns, clean, and eradicate bacteria. Using this kind of technology in hospitals allows faster productivity without
sacrificing patient safety or infection control. The IoT will simplify real-time object monitoring, fast data collecting, and
person-based enhanced care provision. Technology not only helps hospitals better handle their resources but also helps
patients get better care by letting doctors continuously monitor their health and treat them automatically. But before the
IoT can be fully used in the healthcare industry, issues of security and hardware upgrading must be resolved (Porkodi
and Kesavaraja, 2021; Fig. 3.8).
Social robots capable of continuing conversations, identifying human emotions, and providing useful help based on pragmatic need.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
help based on pragmatic need. Patients could be reminded, for example, of following their advice or picking up their
prescription, or of completing rehabilitation programs. In the future, social robots might become more and more crucial
in providing companionship and helping to solve mental health problems. Given the expected ongoing increase in elder
care demand, this is especially true (Tavakoli et al., 2020). A gem designed to assist patients with daily chores such as
collecting objects or traversing their surroundings, this little wheeled robot. Like Pearl, the mobile robot known as
Myon can do a variety of functions, including drug distribution and reminder provision. Designed especially for people
with autism or dementia, Aibo is a robotic dog meant for use as a friend or therapeutic aid. Robear is a humanoid robot
meant for use in patient care. It can enable patients to move about so they can improve or raise their level of treatment (
Tulsulkar et al., 2021; Silvera-Tawil, 2024).
3.5 Technological components of smart robotics
“Smart robots” are a convergence of several creative technologies within the healthcare industry that enable them to
either entirely or partially execute difficult tasks independently. The fundamental elements of these systems are
actuators, data processing units, and sensors. Moreover, when working together, they enable robots to investigate their
surroundings, make fast judgments, and carry out specific duties. These are the fundamental technological parts
enabling the intelligent robot to run in medical surroundings.
3.5.1 IOT-aided robot technology in healthcare
The third most Internet of Things (IoT) embracing industry worldwide is healthcare. This shows its crucial relevance
for better operational effectiveness and patient outcomes. The real-time data exchange made possible by the Internet of
Things (IoT) helps doctors to evaluate a patient's situation. Improved decisions, less needless hospital visits, and
improved general health care may all be achieved with this technology, helping individuals. Automated record keeping
and data sharing also help to increase healthcare efficiency by means of teamwork among different care teams shown in
Fig. 3.8 (Pradhan et al., 2021). The image shows some of the most important uses of mobile robots in medical
environments. These people have duties in cleanup, food and medicine distribution, behavior monitoring, including
mask wearing and social distancing, and hospital running help. These robots also help to treat heart disease, enforce
lockdowns, clean, and eradicate bacteria. Using this kind of technology in hospitals allows faster productivity without
sacrificing patient safety or infection control. The IoT will simplify real-time object monitoring, fast data collecting, and
person-based enhanced care provision. Technology not only helps hospitals better handle their resources but also helps
patients get better care by letting doctors continuously monitor their health and treat them automatically. But before the
IoT can be fully used in the healthcare industry, issues of security and hardware upgrading must be resolved (Porkodi
and Kesavaraja, 2021; Fig. 3.8).
Social robots capable of continuing conversations, identifying human emotions, and providing useful help based on pragmatic need.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
3.5.2 Sensors (visual, auditory, physical, and chemical)
The sensors of a health care robot determine its surroundings, thus enabling it to perform a wide range of medical tasks,
thus defining its capacity. Now, with data gathered by such sensors, robots can assist doctors and patients
simultaneously with informed decisions and with the execution of the surgical operation. Medical robots have four
main kinds of sensors: optical, auditory, tactile, and chemical. Robots that do not have visual sensors, such as cameras,
cannot perform any actions that involve environment sensing, object sensing, or navigation of clinical environments.
Robots with 2D and 3D cameras allow medical instruments and patients to be interacted with more effectively in that
they can perceive depth, texture, and movement. High-resolution cameras on robots enable surgeons to view the
operative field clearly in real time shown in Fig. 3.9 (Din and Paul, 2019). Some of the other applications for visual
sensors hybridized with machine vision technology include medical diagnostics, imaging anomaly detection, and
autonomous navigation of hospital corridors. Because of the microphone sensors, among others, robots can converse in
real life, recognize human voices, and carry out command-like duties. Patients with disabilities or social robots for
elderly care hold highly the existence of such sensors. Robots with NLP can recognize voice commands while
communicating with patients, thus offering them psychological support and consolation. Regarding distress signals,
robots with hearing sensors can help them discern sirens or pleas for help (Al-kahtani et al., 2022; Fig. 3.9).
Figure 3.8
Alt-Text - Short Description: A circular infographic showing various applications of technology in healthcare and
pandemic management.
Alt-Text - Long Description: The circular infographic illustrates various applications of technology in healthcare and
pandemic management. The sections are: Hospital management, Elderly and Physically impaired care, Disinfecting and
cleaning, Delivery of food and medicine, Quarantine Management, Detect wearing of mask and social distancing and Heart
Disease. Each section is represented with an icon related to the specific application.
IOT-aided robot technology in healthcare. Various applications of IoT-aided technology.
Figure 3.9
Alt-Text - Short Description: A diagram illustrating the design and optimization process, unique properties and medical
applications of a technology.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
The sensors of a health care robot determine its surroundings, thus enabling it to perform a wide range of medical tasks,
thus defining its capacity. Now, with data gathered by such sensors, robots can assist doctors and patients
simultaneously with informed decisions and with the execution of the surgical operation. Medical robots have four
main kinds of sensors: optical, auditory, tactile, and chemical. Robots that do not have visual sensors, such as cameras,
cannot perform any actions that involve environment sensing, object sensing, or navigation of clinical environments.
Robots with 2D and 3D cameras allow medical instruments and patients to be interacted with more effectively in that
they can perceive depth, texture, and movement. High-resolution cameras on robots enable surgeons to view the
operative field clearly in real time shown in Fig. 3.9 (Din and Paul, 2019). Some of the other applications for visual
sensors hybridized with machine vision technology include medical diagnostics, imaging anomaly detection, and
autonomous navigation of hospital corridors. Because of the microphone sensors, among others, robots can converse in
real life, recognize human voices, and carry out command-like duties. Patients with disabilities or social robots for
elderly care hold highly the existence of such sensors. Robots with NLP can recognize voice commands while
communicating with patients, thus offering them psychological support and consolation. Regarding distress signals,
robots with hearing sensors can help them discern sirens or pleas for help (Al-kahtani et al., 2022; Fig. 3.9).
Figure 3.8
Alt-Text - Short Description: A circular infographic showing various applications of technology in healthcare and
pandemic management.
Alt-Text - Long Description: The circular infographic illustrates various applications of technology in healthcare and
pandemic management. The sections are: Hospital management, Elderly and Physically impaired care, Disinfecting and
cleaning, Delivery of food and medicine, Quarantine Management, Detect wearing of mask and social distancing and Heart
Disease. Each section is represented with an icon related to the specific application.
IOT-aided robot technology in healthcare. Various applications of IoT-aided technology.
Figure 3.9
Alt-Text - Short Description: A diagram illustrating the design and optimization process, unique properties and medical
applications of a technology.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Robots can sense and react to touch, pressure, or force through physical sensors, thereby ensuring safe patient and
caregiver contact. Such sensors enable rehabilitation robots to vary the degree of support when performing various
exercises for physical therapy, depending on everyone's unique needs and abilities. Force sensors may make surgeons
more precise during sensitive procedures and reduce the risk of damage to tissues in operating rooms because of haptic
feedback. This brings the robotic treatment to being safer and more effective due to the introduction of this other tactile
input. Chemical sensors on the hospital-bound robots can keep track of biochemical markers or detect possibly
dangerous chemicals surrounding the patient. Microbots deployed for targeted drug delivery frequently rely on sensors
that recognize pH or glucose fluctuation to provide drugs. Chemical sensors also have applications in the monitoring
systems of patients. With continuous recording of vital signs and metabolic changes, the devices enable clinicians to
identify possible health problems before their condition worsens. As evident in the image above, a focus on the
application of sensor-based technology in healthcare draws out several aspects and capabilities. By focusing on the
contributions of structural designs, energy harvesting, and materials engineering, it shows how part optimization and
design work in contemporary medical applications. Self-healing, conformable, and stretchable medical devices are the
foundation for possible flexible and long-lasting health solutions. Medical applications range from the registration of
vital signs to recording electrograms, and then there is the treatment of chronic diseases, to the usage of electroceutical
devices interacting with the body bioelectrical systems. Health care professionals may improve patient care through
more accurate monitoring, quicker interventions, and personalized treatments, among other ways (Mois and Beer, 2020
).
3.5.3 Data processing and analytics
Data processing and analytics in healthcare robotics: critical features that enhance the decision-making capabilities of
smart healthcare robots include data processing and analytics. These systems have incorporated algorithms from AI and
ML, where they process sensor data in real time, adapt to changing tasks, and learn continually from previous
exposures (Sikdar and Guha, 2020). This facilitates the execution of important healthcare functions in the care of
patients, surgery, rehabilitation, and diagnostics with greater speed and accuracy. For example, in the medical sector,
robots must process highly voluminous data for instantaneous decisions. For instance, surgical robots, like the Da Vinci
system, rely on highly sophisticated imaging and data-processing algorithms that provide instantaneous feedback to the
surgeons for precise movement (Allam, 2023). Motion planning in hospital robots relies on sensors like LiDAR or
Alt-Text - Long Description: The diagram is divided into three main sections: Design and Optimization, Unique Properties
and Medical Applications. The Design and Optimization section includes Energy Harvesting, Materials Engineering and
Structural Designs. The Unique Properties section includes Stretchability, Conformability and Self-Healing. The Medical
Applications section includes Electrograms, Chronic disease monitoring and Electroceutical. Additionally, there are
Healthcare Applications which include Vital sign Monitoring, Activity Monitoring and Biomolecular State Monitoring.
Sensors: various visual, auditory, physical, and chemical sensors. Unique qualities: Wearable gadgets may be easily integrated into
the body because of their to their stretchability, compliance, and self-healing. Wearable gadgets use body heat, light, and movement
to generate energy, reducing battery replacement. Material Engineering, These gadgets are composed of lightweight, flexible,
biocompatible modern materials. Design structures: Innovative gadget designs provide long-term comfort and durability. Medical
uses: These devices capture electrograms, monitor chronic diseases, and provide electroceuticals. Applications in healthcare: Vital
sign monitoring, activity tracking, and biomolecular state sensing help comprehend health and well-being.
caregiver contact. Such sensors enable rehabilitation robots to vary the degree of support when performing various
exercises for physical therapy, depending on everyone's unique needs and abilities. Force sensors may make surgeons
more precise during sensitive procedures and reduce the risk of damage to tissues in operating rooms because of haptic
feedback. This brings the robotic treatment to being safer and more effective due to the introduction of this other tactile
input. Chemical sensors on the hospital-bound robots can keep track of biochemical markers or detect possibly
dangerous chemicals surrounding the patient. Microbots deployed for targeted drug delivery frequently rely on sensors
that recognize pH or glucose fluctuation to provide drugs. Chemical sensors also have applications in the monitoring
systems of patients. With continuous recording of vital signs and metabolic changes, the devices enable clinicians to
identify possible health problems before their condition worsens. As evident in the image above, a focus on the
application of sensor-based technology in healthcare draws out several aspects and capabilities. By focusing on the
contributions of structural designs, energy harvesting, and materials engineering, it shows how part optimization and
design work in contemporary medical applications. Self-healing, conformable, and stretchable medical devices are the
foundation for possible flexible and long-lasting health solutions. Medical applications range from the registration of
vital signs to recording electrograms, and then there is the treatment of chronic diseases, to the usage of electroceutical
devices interacting with the body bioelectrical systems. Health care professionals may improve patient care through
more accurate monitoring, quicker interventions, and personalized treatments, among other ways (Mois and Beer, 2020
).
3.5.3 Data processing and analytics
Data processing and analytics in healthcare robotics: critical features that enhance the decision-making capabilities of
smart healthcare robots include data processing and analytics. These systems have incorporated algorithms from AI and
ML, where they process sensor data in real time, adapt to changing tasks, and learn continually from previous
exposures (Sikdar and Guha, 2020). This facilitates the execution of important healthcare functions in the care of
patients, surgery, rehabilitation, and diagnostics with greater speed and accuracy. For example, in the medical sector,
robots must process highly voluminous data for instantaneous decisions. For instance, surgical robots, like the Da Vinci
system, rely on highly sophisticated imaging and data-processing algorithms that provide instantaneous feedback to the
surgeons for precise movement (Allam, 2023). Motion planning in hospital robots relies on sensors like LiDAR or
Alt-Text - Long Description: The diagram is divided into three main sections: Design and Optimization, Unique Properties
and Medical Applications. The Design and Optimization section includes Energy Harvesting, Materials Engineering and
Structural Designs. The Unique Properties section includes Stretchability, Conformability and Self-Healing. The Medical
Applications section includes Electrograms, Chronic disease monitoring and Electroceutical. Additionally, there are
Healthcare Applications which include Vital sign Monitoring, Activity Monitoring and Biomolecular State Monitoring.
Sensors: various visual, auditory, physical, and chemical sensors. Unique qualities: Wearable gadgets may be easily integrated into
the body because of their to their stretchability, compliance, and self-healing. Wearable gadgets use body heat, light, and movement
to generate energy, reducing battery replacement. Material Engineering, These gadgets are composed of lightweight, flexible,
biocompatible modern materials. Design structures: Innovative gadget designs provide long-term comfort and durability. Medical
uses: These devices capture electrograms, monitor chronic diseases, and provide electroceuticals. Applications in healthcare: Vital
sign monitoring, activity tracking, and biomolecular state sensing help comprehend health and well-being.
cameras to avoid obstacles and adapt to changing environments. This is what allows robots to become safe and
effective in a clinical space: the ability to process in real time (Sharma et al., 2022). AI and ML within health care and
medical devices constantly enhance these machines over time through data-driven learning. From the rehabilitative
standpoint, AI end-robots assess patient information, which may include muscle strength and movement, and make
necessary adjustments to therapy sessions based on the needs of each patient. In diagnostic robots, ML plays a crucial
role as an input tool, scanning medical images and genetic information to detect disease-related patterns, thereby
significantly improving diagnosis accuracy. It enables healthcare providers to diagnose patients more quickly and with
greater knowledge (Bodo et al., 2013; Fig. 3.10).
Cloud computing and big data analytics: cloud computing serves as a significant enabler for data generated by
healthcare robots. By using cloud-based infrastructures, robots are also able to manage and process huge datasets faster
and with higher accessibility. For instance, a health robot may be utilizing cloud-based databases to compare the
medical history of a patient with similar cases, aiding in diagnosis and tailor-made treatment plans. Cloud computing
also offers remote monitoring and control of robotic systems. Thus, the health professionals can monitor the robots'
activities and the general treatment of patients from a distance.
Predictive analytics: it will utilize predictive analytics or advanced data analysis to make predictions based on historical
trends in the data. For example, it could serve as a digital twin, a digital representation of a physical robot shown in Fig.
3.10 (Huang et al., 2023). This basically uses predictive analytics to simulate real-time operations for the robotic
operations during maintenance to identify technical issues early and have overall better performance from the robot
during those maintenance periods. Predictive analytics also enables the prediction of patient outcomes, prevents
equipment failures, and even predicts a disease outbreak, thereby further enhancing the safety and effectiveness of
healthcare robotics (Allam, 2022).
3.5.4 Actuators and robotics control systems
Figure 3.10
Alt-Text - Short Description: A flowchart showing the process from fast data integration to actionable insights.
Alt-Text - Long Description: The flowchart describes a process with the following steps: Fast data Integration, Situational
and contextual awareness, Signal processing and feature extraction and Actionable Insights. Each step is connected to specific
data types or analysis methods. Fast data Integration is linked to EKG, ABP, pulse Ox and so forth. Situational and
contextual awareness is linked to Demography, lab allergies, meds and so forth. Signal processing and feature extraction is
linked to Linear, nonlinear, multidomain analysis. Actionable Insights is linked to Diagnostic, predictive, prescriptive.
Data processing and analysis. It collects physiologic data: ECG, BP, pulse oximetry, etc. It correlates information gathered with the
history of the patient and his environment. It uses machine learning and high-performance signal processing for data analysis. It offers
practical insights with an early detection process along with intervention. This leads to better patient care and reduced health service
cost.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
effective in a clinical space: the ability to process in real time (Sharma et al., 2022). AI and ML within health care and
medical devices constantly enhance these machines over time through data-driven learning. From the rehabilitative
standpoint, AI end-robots assess patient information, which may include muscle strength and movement, and make
necessary adjustments to therapy sessions based on the needs of each patient. In diagnostic robots, ML plays a crucial
role as an input tool, scanning medical images and genetic information to detect disease-related patterns, thereby
significantly improving diagnosis accuracy. It enables healthcare providers to diagnose patients more quickly and with
greater knowledge (Bodo et al., 2013; Fig. 3.10).
Cloud computing and big data analytics: cloud computing serves as a significant enabler for data generated by
healthcare robots. By using cloud-based infrastructures, robots are also able to manage and process huge datasets faster
and with higher accessibility. For instance, a health robot may be utilizing cloud-based databases to compare the
medical history of a patient with similar cases, aiding in diagnosis and tailor-made treatment plans. Cloud computing
also offers remote monitoring and control of robotic systems. Thus, the health professionals can monitor the robots'
activities and the general treatment of patients from a distance.
Predictive analytics: it will utilize predictive analytics or advanced data analysis to make predictions based on historical
trends in the data. For example, it could serve as a digital twin, a digital representation of a physical robot shown in Fig.
3.10 (Huang et al., 2023). This basically uses predictive analytics to simulate real-time operations for the robotic
operations during maintenance to identify technical issues early and have overall better performance from the robot
during those maintenance periods. Predictive analytics also enables the prediction of patient outcomes, prevents
equipment failures, and even predicts a disease outbreak, thereby further enhancing the safety and effectiveness of
healthcare robotics (Allam, 2022).
3.5.4 Actuators and robotics control systems
Figure 3.10
Alt-Text - Short Description: A flowchart showing the process from fast data integration to actionable insights.
Alt-Text - Long Description: The flowchart describes a process with the following steps: Fast data Integration, Situational
and contextual awareness, Signal processing and feature extraction and Actionable Insights. Each step is connected to specific
data types or analysis methods. Fast data Integration is linked to EKG, ABP, pulse Ox and so forth. Situational and
contextual awareness is linked to Demography, lab allergies, meds and so forth. Signal processing and feature extraction is
linked to Linear, nonlinear, multidomain analysis. Actionable Insights is linked to Diagnostic, predictive, prescriptive.
Data processing and analysis. It collects physiologic data: ECG, BP, pulse oximetry, etc. It correlates information gathered with the
history of the patient and his environment. It uses machine learning and high-performance signal processing for data analysis. It offers
practical insights with an early detection process along with intervention. This leads to better patient care and reduced health service
cost.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Actuators refer to the parts that cause robots to move and act physically by converting electrical signals into mechanical
motion. With respect to a medical robot, precision and control are necessary factors in ensuring safe and effective
functioning, especially in the interaction with delicate human tissues or helping patients in rehabilitation.
Electromechanical actuators: These actuators convert electrical energy into mechanical movement and thus are often
used in surgical robots to enable control of the robotic arms. For example, in the case of the da Vinci system,
electromechanical actuators can precisely move surgical instruments under smooth operation. The actuators can reflect
hand motions by the surgeon through the robotic arms, thereby offering effective dexterity and accuracy.
Hydraulic and pneumatic actuators: Other robotic systems have tasks requiring higher levels of force or torque, mainly
using hydraulic and pneumatic actuators. The hydraulic actuators are widely applied in rehabilitation robots, where the
aid of a robotic system is provided to the patients for repetitive physical exercises, such as walking or lifting heavy
loads. Hydraulic actuators produce fluent and high-power movements of fluids and end fluids because of its pressure;
this enables patients to recover motor skills after surgery or injury (Wang et al., 2024).
Soft actuators: Soft actuators are one of the emerging robotics technologies that are under development to mimic the
softness and flexibility of human muscles. Such actuators become important for applications where safety in human
interaction is a prerequisite, such as social robots or physical therapy assistance robots. Soft robotics could minimize the
potential injury to patients in rehabilitation or other direct-contact activities.
Robotics control systems are the brains of a robotic system; they order the actuators to move based on sensor data
given. In healthcare, control systems ensure that robotic actions are implemented correctly, safely, and efficiently. For
example, the control system for a surgeon robot working on a patient must be of incredibly high complexity because
every motion needs to be synchronized with the input of the surgeon and real-time feedback from sensors. Similar
considerations also apply to mobile robots’ autonomous navigation requires continuously adapting movement in real
time to sensor data (Ikpe et al., 2024).
3.6 Applications of smart robotics in healthcare
Smart robotics has been applied in diverse and impactful ways, changing the face of how procedures are carried out
and how patients receive care. They help the healthcare provider surgeon in surgeries, monitoring patients,
administering treatments, and assisting in the rehabilitation of the patient. Some of the most visible applications of smart
robotics in healthcare include enhancement of patient outcomes, making clinical workflows more streamlined, and
ensuring increased safety for patients (Akpakwu et al., 2018; Li, 2019; Table 3.1).
Alt-Text - Short Description: Table 31
Table 3.1
Applications of smart robotics in healthcare.
S.
no.
Robot name YearReferencesSensors Applications Remarks
1
Da Vinci Surgical
system
2000
Kim et al.
(2024)
Optical sensors,
force sensors, and
vision sensors
Robotic-assisted
surgeries
Enhances precision in
minimally invasive
procedures.
2Mako-Robotic Arm2006
Roche
(2021)
Force sensors and
3D image sensors
Orthopedic surgeries
(joint replacements)
Improves accuracy of implant
placement
3CyberKnife 2001Kilby et al.
(2020)
Motion sensors and
image sensors
Radiation therapy Targets tumors with high
precision, minimizing damage
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To view the actual presentation of the table, please click on the
located at the top of the page.
motion. With respect to a medical robot, precision and control are necessary factors in ensuring safe and effective
functioning, especially in the interaction with delicate human tissues or helping patients in rehabilitation.
Electromechanical actuators: These actuators convert electrical energy into mechanical movement and thus are often
used in surgical robots to enable control of the robotic arms. For example, in the case of the da Vinci system,
electromechanical actuators can precisely move surgical instruments under smooth operation. The actuators can reflect
hand motions by the surgeon through the robotic arms, thereby offering effective dexterity and accuracy.
Hydraulic and pneumatic actuators: Other robotic systems have tasks requiring higher levels of force or torque, mainly
using hydraulic and pneumatic actuators. The hydraulic actuators are widely applied in rehabilitation robots, where the
aid of a robotic system is provided to the patients for repetitive physical exercises, such as walking or lifting heavy
loads. Hydraulic actuators produce fluent and high-power movements of fluids and end fluids because of its pressure;
this enables patients to recover motor skills after surgery or injury (Wang et al., 2024).
Soft actuators: Soft actuators are one of the emerging robotics technologies that are under development to mimic the
softness and flexibility of human muscles. Such actuators become important for applications where safety in human
interaction is a prerequisite, such as social robots or physical therapy assistance robots. Soft robotics could minimize the
potential injury to patients in rehabilitation or other direct-contact activities.
Robotics control systems are the brains of a robotic system; they order the actuators to move based on sensor data
given. In healthcare, control systems ensure that robotic actions are implemented correctly, safely, and efficiently. For
example, the control system for a surgeon robot working on a patient must be of incredibly high complexity because
every motion needs to be synchronized with the input of the surgeon and real-time feedback from sensors. Similar
considerations also apply to mobile robots’ autonomous navigation requires continuously adapting movement in real
time to sensor data (Ikpe et al., 2024).
3.6 Applications of smart robotics in healthcare
Smart robotics has been applied in diverse and impactful ways, changing the face of how procedures are carried out
and how patients receive care. They help the healthcare provider surgeon in surgeries, monitoring patients,
administering treatments, and assisting in the rehabilitation of the patient. Some of the most visible applications of smart
robotics in healthcare include enhancement of patient outcomes, making clinical workflows more streamlined, and
ensuring increased safety for patients (Akpakwu et al., 2018; Li, 2019; Table 3.1).
Alt-Text - Short Description: Table 31
Table 3.1
Applications of smart robotics in healthcare.
S.
no.
Robot name YearReferencesSensors Applications Remarks
1
Da Vinci Surgical
system
2000
Kim et al.
(2024)
Optical sensors,
force sensors, and
vision sensors
Robotic-assisted
surgeries
Enhances precision in
minimally invasive
procedures.
2Mako-Robotic Arm2006
Roche
(2021)
Force sensors and
3D image sensors
Orthopedic surgeries
(joint replacements)
Improves accuracy of implant
placement
3CyberKnife 2001Kilby et al.
(2020)
Motion sensors and
image sensors
Radiation therapy Targets tumors with high
precision, minimizing damage
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To view the actual presentation of the table, please click on the
located at the top of the page.
3.6.1 Operating room assistance
to healthy tissue
4
ROSA (Robotic
Surgical Assistant)
2010
Bonda et al.
(2020)
Cameras and
ultrasonic sensors
Neurosurgery
Assists in precise positioning
during brain surgeries
5TUG Robot 2004
Bloss
(2011)
Proximity sensors
and cameras
Hospital logistics
(delivery of
medications and
supplies)
Automates transport within
hospitals, reducing staff
workload
6
RIBA (Robot for
Interactive Body
Assistance)
2015
Mukai et al.
(2011)
Tactile sensors and
cameras
Rehabilitation
Assists patients in physical
therapy and rehabilitation
exercises
7iRobot's Ava 2016
Lewis et al.
(2014)
Cameras and
ultrasonic sensors
Telepresence and
remote consultation
Facilitates remote doctor-
patient interactions
8Savioke Relay 2015
Pransky
(2016)
Proximity sensors
and cameras
Hospital delivery
services
Delivers items like
medications and meals
autonomously
9Medrobotics Flex2014
Remacle
et al. (2015)
Cameras and force
sensors
Minimally invasive
surgeries
Provides flexible robotic
assistance for complex
procedures
10KUKA LBR iiwa 2013
Niu et al.
(2021)
Force–torque
sensors
Collaborative tasks in
surgery
Works alongside surgeons for
precision tasks
11Myomo 2008
Vieira et al.
(2022)
Motion sensors
Rehabilitation for
stroke patients
Assists with movement in
paralyzed limbs using
electrical stimulation
12Aethon 2004
Liszewski
2024)
LIDAR and camerasAutomated transport
Transports supplies and
medications within healthcare
facilities
13Paro Robot 2003
Kang et al.
(2020)
Touch sensors and
cameras
Therapeutic robot for
dementia care
Provides comfort and
companionship to patients
with dementia
14Vicarious Surgical2020
Evans et al.
(2021)
Cameras and tactile
sensors
Robotic-assisted
surgeries
Mimics human hand
movements for surgical
precision
15
SoftBank Robotics
Pepper
2014
Mezzina
and De
Venuto
(2021)
Cameras and
microphones
Patient interaction and
information
Engages with patients and
provides information in
healthcare settings.
16Zora Bots 2016
Huisman
and Kort
(2019)
Cameras and touch
screens
Therapy and
rehabilitation
Therapy and rehabilitation
17
Siemens
Healthiness ARTIS
pheno
2017
Cheng et al.
(2020)
Imaging sensors
Interventional
radiology
Provides advanced imaging
capabilities during procedures.
18VBot 2021
Essel et al.
(2024)
Proximity sensorsPatient monitoring
Monitors patient vitals and
alerts healthcare staff when
needed
19Giraff Plus 2012
Coradeschi
et al. (2014)
Cameras and
microphones
Telepresence
Enables remote consultations
for elderly or disabled patients
20
Diligent Robotics’
Moxi
2020
Aydınocak
(2023)
Cameras and
LIDAR
Logistics support
Automates supply delivery
tasks within hospitals to free
up staff time
to healthy tissue
4
ROSA (Robotic
Surgical Assistant)
2010
Bonda et al.
(2020)
Cameras and
ultrasonic sensors
Neurosurgery
Assists in precise positioning
during brain surgeries
5TUG Robot 2004
Bloss
(2011)
Proximity sensors
and cameras
Hospital logistics
(delivery of
medications and
supplies)
Automates transport within
hospitals, reducing staff
workload
6
RIBA (Robot for
Interactive Body
Assistance)
2015
Mukai et al.
(2011)
Tactile sensors and
cameras
Rehabilitation
Assists patients in physical
therapy and rehabilitation
exercises
7iRobot's Ava 2016
Lewis et al.
(2014)
Cameras and
ultrasonic sensors
Telepresence and
remote consultation
Facilitates remote doctor-
patient interactions
8Savioke Relay 2015
Pransky
(2016)
Proximity sensors
and cameras
Hospital delivery
services
Delivers items like
medications and meals
autonomously
9Medrobotics Flex2014
Remacle
et al. (2015)
Cameras and force
sensors
Minimally invasive
surgeries
Provides flexible robotic
assistance for complex
procedures
10KUKA LBR iiwa 2013
Niu et al.
(2021)
Force–torque
sensors
Collaborative tasks in
surgery
Works alongside surgeons for
precision tasks
11Myomo 2008
Vieira et al.
(2022)
Motion sensors
Rehabilitation for
stroke patients
Assists with movement in
paralyzed limbs using
electrical stimulation
12Aethon 2004
Liszewski
2024)
LIDAR and camerasAutomated transport
Transports supplies and
medications within healthcare
facilities
13Paro Robot 2003
Kang et al.
(2020)
Touch sensors and
cameras
Therapeutic robot for
dementia care
Provides comfort and
companionship to patients
with dementia
14Vicarious Surgical2020
Evans et al.
(2021)
Cameras and tactile
sensors
Robotic-assisted
surgeries
Mimics human hand
movements for surgical
precision
15
SoftBank Robotics
Pepper
2014
Mezzina
and De
Venuto
(2021)
Cameras and
microphones
Patient interaction and
information
Engages with patients and
provides information in
healthcare settings.
16Zora Bots 2016
Huisman
and Kort
(2019)
Cameras and touch
screens
Therapy and
rehabilitation
Therapy and rehabilitation
17
Siemens
Healthiness ARTIS
pheno
2017
Cheng et al.
(2020)
Imaging sensors
Interventional
radiology
Provides advanced imaging
capabilities during procedures.
18VBot 2021
Essel et al.
(2024)
Proximity sensorsPatient monitoring
Monitors patient vitals and
alerts healthcare staff when
needed
19Giraff Plus 2012
Coradeschi
et al. (2014)
Cameras and
microphones
Telepresence
Enables remote consultations
for elderly or disabled patients
20
Diligent Robotics’
Moxi
2020
Aydınocak
(2023)
Cameras and
LIDAR
Logistics support
Automates supply delivery
tasks within hospitals to free
up staff time
Smart surgical robots have fundamentally changed the operating room by increasing the precision and dexterity of
surgery, especially in cases involving minimally invasive surgery. It is through such robots, for instance, as the da Vinci
Surgical System and Mako robotic-arm assisted surgery, that high-definition, three-dimensional visualization of the site
of surgery can be maintained and controlled, with stable movement better than can be managed with human hands
alone. These systems can perform intricate procedures, including heart, prostate, and gynecological surgeries with
minimal incisions and therefore less threat of infections, blood loss, and complications from surgery. The robotic
surgery also makes remote surgery possible, whereby a surgeon could perform an operation from a distant location, an
ability that was notably tested during the COVID-19 pandemic. The very high precision of control diminishes the
physical burden on surgeons, thus enabling longer and more complicated procedures in the case of robotic systems.
Additionally, robotic systems are increasingly composed of AI-driven algorithms that help in decision-making during
the optimization of the path adopted by the surgeon through an operation or predicting actual complications in real-time
(Pasquer et al., 2024; Zhang et al., 2024).
3.6.2 ICU and emergency applications
They have been very useful in handling high-risk, repetitive tasks and relieving the workload of healthcare
professionals in ICUs and emergency departments. In the recent COVID-19 pandemic, mobile robots largely
contributed to the decontamination of hospital environments, handling medical prescription, monitoring patients, and
significantly lowering the risk of the virus's transmission to healthcare staff (Chen and Wang, 2021). These robots are
equipped with ultraviolet (UV) light or hydrogen peroxide vapor systems for the sterilization of surfaces, hence
preventing direct exposure of humans to pathogens. Robots equipped with telemedicine systems have been applied in
emergencies to evaluate patients at a distance, and healthcare professionals can judge signs of life and make preliminary
diagnoses safely. Telepresence robots supported with cameras and microphones enable patients and physicians to
consult in real time, while they will also simplify the emergency response system. It will also assist in triaging the
patients and take them for preliminary assessments before the intervention of human medical staff. The robotic systems
of the ICU can monitor patients at real-time with the use of sensors for essential signs such as oxygen levels and so on.
These robots give real-time data and alerts, which would provide patient safety through the improvement of early
intervention about a patient's condition when it worsens (Khamis et al., 2021).
3.6.3 Rehabilitation and physiotherapy
Rehabilitation robots allow patients to recover from injuries, surgeries, or neurological conditions by aiding the patient
in physical exercises and motor activities. Such robots provide repetitive, accurate movements to the patients to ensure
adequate recovery from stroke, spinal cord injuries, or other forms of orthopedic surgery. Examples of Lokomat and
Armeo systems used in rehabilitation clinics to assist in gait and upper limb rehabilitation. For example, Lokomat
accompanies patients as they start to regain steps by wearing a robotic exoskeleton that stabilizes and supports the legs
while a harness helps the body's weight. The system adjusts to the level of the patient's achievement in real time, so the
gait can be practiced early and with intensity. Armeo is a supportive, adjustable support given during physical exercises
for the re-establishment of arm movements after stroke or trauma. A course of physiotherapy that delivers a degree of
precision and strength that sometimes cannot be afforded by the human therapist. Robots can coach patients through
individualized rehabilitation patterns, provide feedback to guide them across each step, increase or decrease the
intensity of exercises based on the instant feedback received, and thus deliver recovery programs designed specifically
for individual needs to maximize recovery endpoints (Holland et al., 2021).
3.6.4 Patient monitoring and care support
Patient monitoring and care support robots are supposed to give ongoing monitoring of patient conditions, especially in
chronic cases or those requiring long-term care. The robots can be equipped with sensors for monitoring heart rate,
blood pressure, glucose levels, and oxygen saturation. Alerts can be sent out to healthcare professionals when abnormal
readings are detected, thus enabling timely intervention. Wearable robots and assistive devices with AI and IoT help
monitor patients from a distance (Bohr and Memarzadeh, 2020). This is highly beneficial for elderly patients or patients
having chronic diseases, where frequent check-ups are required, but they do not need to be kept in the hospital. All this
reduces the stress on health care facilities as it offers distant management of patients. With this type of management, live
data can be given to doctors, who can readjust the treatments according to that data. Applications in elderly care
support: in assisting the elderly with personal activities such as reminding patients about their medication, assisting
surgery, especially in cases involving minimally invasive surgery. It is through such robots, for instance, as the da Vinci
Surgical System and Mako robotic-arm assisted surgery, that high-definition, three-dimensional visualization of the site
of surgery can be maintained and controlled, with stable movement better than can be managed with human hands
alone. These systems can perform intricate procedures, including heart, prostate, and gynecological surgeries with
minimal incisions and therefore less threat of infections, blood loss, and complications from surgery. The robotic
surgery also makes remote surgery possible, whereby a surgeon could perform an operation from a distant location, an
ability that was notably tested during the COVID-19 pandemic. The very high precision of control diminishes the
physical burden on surgeons, thus enabling longer and more complicated procedures in the case of robotic systems.
Additionally, robotic systems are increasingly composed of AI-driven algorithms that help in decision-making during
the optimization of the path adopted by the surgeon through an operation or predicting actual complications in real-time
(Pasquer et al., 2024; Zhang et al., 2024).
3.6.2 ICU and emergency applications
They have been very useful in handling high-risk, repetitive tasks and relieving the workload of healthcare
professionals in ICUs and emergency departments. In the recent COVID-19 pandemic, mobile robots largely
contributed to the decontamination of hospital environments, handling medical prescription, monitoring patients, and
significantly lowering the risk of the virus's transmission to healthcare staff (Chen and Wang, 2021). These robots are
equipped with ultraviolet (UV) light or hydrogen peroxide vapor systems for the sterilization of surfaces, hence
preventing direct exposure of humans to pathogens. Robots equipped with telemedicine systems have been applied in
emergencies to evaluate patients at a distance, and healthcare professionals can judge signs of life and make preliminary
diagnoses safely. Telepresence robots supported with cameras and microphones enable patients and physicians to
consult in real time, while they will also simplify the emergency response system. It will also assist in triaging the
patients and take them for preliminary assessments before the intervention of human medical staff. The robotic systems
of the ICU can monitor patients at real-time with the use of sensors for essential signs such as oxygen levels and so on.
These robots give real-time data and alerts, which would provide patient safety through the improvement of early
intervention about a patient's condition when it worsens (Khamis et al., 2021).
3.6.3 Rehabilitation and physiotherapy
Rehabilitation robots allow patients to recover from injuries, surgeries, or neurological conditions by aiding the patient
in physical exercises and motor activities. Such robots provide repetitive, accurate movements to the patients to ensure
adequate recovery from stroke, spinal cord injuries, or other forms of orthopedic surgery. Examples of Lokomat and
Armeo systems used in rehabilitation clinics to assist in gait and upper limb rehabilitation. For example, Lokomat
accompanies patients as they start to regain steps by wearing a robotic exoskeleton that stabilizes and supports the legs
while a harness helps the body's weight. The system adjusts to the level of the patient's achievement in real time, so the
gait can be practiced early and with intensity. Armeo is a supportive, adjustable support given during physical exercises
for the re-establishment of arm movements after stroke or trauma. A course of physiotherapy that delivers a degree of
precision and strength that sometimes cannot be afforded by the human therapist. Robots can coach patients through
individualized rehabilitation patterns, provide feedback to guide them across each step, increase or decrease the
intensity of exercises based on the instant feedback received, and thus deliver recovery programs designed specifically
for individual needs to maximize recovery endpoints (Holland et al., 2021).
3.6.4 Patient monitoring and care support
Patient monitoring and care support robots are supposed to give ongoing monitoring of patient conditions, especially in
chronic cases or those requiring long-term care. The robots can be equipped with sensors for monitoring heart rate,
blood pressure, glucose levels, and oxygen saturation. Alerts can be sent out to healthcare professionals when abnormal
readings are detected, thus enabling timely intervention. Wearable robots and assistive devices with AI and IoT help
monitor patients from a distance (Bohr and Memarzadeh, 2020). This is highly beneficial for elderly patients or patients
having chronic diseases, where frequent check-ups are required, but they do not need to be kept in the hospital. All this
reduces the stress on health care facilities as it offers distant management of patients. With this type of management, live
data can be given to doctors, who can readjust the treatments according to that data. Applications in elderly care
support: in assisting the elderly with personal activities such as reminding patients about their medication, assisting
mobility, and even social facilitation. Robots such as Pepper, PARO are targeted to provide social contact with their
patients, giving them companionship and emotional contact with people, a key requirement in improving the mental
comfort of patients who are confined or suffering from cognitive impairments (Verma et al., 2022).
3.6.5 Drug delivery using microbots
Perhaps one of the most advanced applications of smart robotics in healthcare is microbots or nanobots for targeted
drug delivery. It is possible to inject microbots into the bloodstream with sizes less than a millimeter, thus they can be
guided to specific tissues or organs where the drug can be delivered and reach a spot without affecting the healthy cells
with side effects. This direct and precise delivery is especially critical with cancer treatments since the old method of
chemotherapy affects both cancerous and normal cells, resulting in particularly unpleasant side effects. Microbots are
routed to the target site either by an external magnetic field or through biological markers, once at the target site, they
can proceed to deliver their drugs in response to a specific physiological cue, for example, altered Ph or temperature;
hence, the drug will be released only when and where it is needed (Selvi et al., 2024).
Another exciting application of microbots is in minimally invasive surgeries where they can execute tasks like clearing
blocked arteries or removing small tumors. The efforts also include developing biohybrid microbots based on biological
materials integrated with synthetic elements, which will be able to navigate the intricate environments of the human
body with great efficiency. While microbots are still very much in their experimental phase, promises for their use in
changing drug delivery and surgical procedures are likely to show real impacts within the next few years (Le and Shim,
2024).
3.7 Case studies
Real-world applications of smart robotics in health care are more significant, as they detail the potential and the effect of
such technology. This part discusses three case studies: how smart robotics is changing the future of surgical
procedures, rehabilitation, and drug delivery processes.
3.7.1 Case study 1: the case study of remote robotic surgery performed in Xinjiang, China
Among the many sectors that have been influenced by technological advancement, the healthcare sector is only one of
them. The utilization of robots in surgical procedures presents opportunities to expand access to medical treatment and
improve the results for patients (Iftikhar et al., 2000Iftikhar et al.; Balaguer-Castro et al., 2023). In this case study, we
analyze a groundbreaking robotic surgery that was performed in Xinjiang, China. The surgeon was able to successfully
operate on a patient while operating from 5000 km and making use of 5G technology. Geographically isolated with
inadequate medical infrastructure, Xinjiang is a rural region of China that finds it difficult to meet the medical needs of
its population. Remote robotic surgery was supposed to be able to alleviate these issues and offer a way for professional
treatment accessibility. A patient in Xinjiang sadly lacked simple access to a cholecystectomy. This was planned to be
corrected remotely via robotic surgery. The Beijing-based 5000-km-distance surgeon managed to reach the Xinjiang
patient using 5G technology. The surgeon carefully controlled the robotic arms as they swung over the procedure
shown in Fig. 3.11. The successful operation verified the feasibility of remote robotic surgery for the provision of
targeted medical treatment to patients residing in far-off areas. Telemedicine and robotic surgery also demonstrated clear
advancement here. The emergence of remote robotic surgery might drastically alter the worldwide provision of
healthcare services (Peralta-Ochoa et al., 2023; Fig. 3.11).
Figure 3.11
Alt-Text - Short Description: Two photos present medical professionals using advanced robotic surgical equipment.
Alt-Text - Long Description: The first image displays a medical professional operating a robotic surgical system, seated at a
console with multiple display screens showing surgical procedures. The second image shows three medical professionals in
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
patients, giving them companionship and emotional contact with people, a key requirement in improving the mental
comfort of patients who are confined or suffering from cognitive impairments (Verma et al., 2022).
3.6.5 Drug delivery using microbots
Perhaps one of the most advanced applications of smart robotics in healthcare is microbots or nanobots for targeted
drug delivery. It is possible to inject microbots into the bloodstream with sizes less than a millimeter, thus they can be
guided to specific tissues or organs where the drug can be delivered and reach a spot without affecting the healthy cells
with side effects. This direct and precise delivery is especially critical with cancer treatments since the old method of
chemotherapy affects both cancerous and normal cells, resulting in particularly unpleasant side effects. Microbots are
routed to the target site either by an external magnetic field or through biological markers, once at the target site, they
can proceed to deliver their drugs in response to a specific physiological cue, for example, altered Ph or temperature;
hence, the drug will be released only when and where it is needed (Selvi et al., 2024).
Another exciting application of microbots is in minimally invasive surgeries where they can execute tasks like clearing
blocked arteries or removing small tumors. The efforts also include developing biohybrid microbots based on biological
materials integrated with synthetic elements, which will be able to navigate the intricate environments of the human
body with great efficiency. While microbots are still very much in their experimental phase, promises for their use in
changing drug delivery and surgical procedures are likely to show real impacts within the next few years (Le and Shim,
2024).
3.7 Case studies
Real-world applications of smart robotics in health care are more significant, as they detail the potential and the effect of
such technology. This part discusses three case studies: how smart robotics is changing the future of surgical
procedures, rehabilitation, and drug delivery processes.
3.7.1 Case study 1: the case study of remote robotic surgery performed in Xinjiang, China
Among the many sectors that have been influenced by technological advancement, the healthcare sector is only one of
them. The utilization of robots in surgical procedures presents opportunities to expand access to medical treatment and
improve the results for patients (Iftikhar et al., 2000Iftikhar et al.; Balaguer-Castro et al., 2023). In this case study, we
analyze a groundbreaking robotic surgery that was performed in Xinjiang, China. The surgeon was able to successfully
operate on a patient while operating from 5000 km and making use of 5G technology. Geographically isolated with
inadequate medical infrastructure, Xinjiang is a rural region of China that finds it difficult to meet the medical needs of
its population. Remote robotic surgery was supposed to be able to alleviate these issues and offer a way for professional
treatment accessibility. A patient in Xinjiang sadly lacked simple access to a cholecystectomy. This was planned to be
corrected remotely via robotic surgery. The Beijing-based 5000-km-distance surgeon managed to reach the Xinjiang
patient using 5G technology. The surgeon carefully controlled the robotic arms as they swung over the procedure
shown in Fig. 3.11. The successful operation verified the feasibility of remote robotic surgery for the provision of
targeted medical treatment to patients residing in far-off areas. Telemedicine and robotic surgery also demonstrated clear
advancement here. The emergence of remote robotic surgery might drastically alter the worldwide provision of
healthcare services (Peralta-Ochoa et al., 2023; Fig. 3.11).
Figure 3.11
Alt-Text - Short Description: Two photos present medical professionals using advanced robotic surgical equipment.
Alt-Text - Long Description: The first image displays a medical professional operating a robotic surgical system, seated at a
console with multiple display screens showing surgical procedures. The second image shows three medical professionals in
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
3.7.2 Case study 2: ROBERT––revolutionizing neurorehabilitation with robotics
Robotics rehabilitation targets the use of robotic technologies to aid in motor therapy of the patient after impairments,
such as stroke, as well as developing aids to support independent life for disabled and elderly people. Recent advances
increasingly involve cognitive aspects of motor control, using brain imaging technologies in making a feedback loop
from the brain to action. Robotics is pivotal in neuro-motor rehabilitation due to the flexible, programmable tools it
presents in making quantitative assessments and treatments possible. Besides motor rehabilitation, robotics also helps
with the mental well-being of seniors. Rapid progressions in assistive technologies for the disabled and elderly are now
merging human-centered design with integrated robotic and micro-mechatronic systems.
It was developed by Life Science Robotics of Denmark and is here now, in partnership with Innovative Rehab
Technologies Queensland start-up. ROBERT is one such mobile robot designed specifically for rehabilitation purposes:
it tackles both the upper and lower limbs, as well as assisting with recovery for patients undergoing neurorehabilitation,
and is currently in pilot stage with a major Queensland hospital. There is a severe shortage of rehabilitation therapists in
Australia, especially in the regional and remote parts of the country. In these areas, access to high-quality, intensive
therapy remains severely limited shown in Fig. 3.12. This lack of it means that patients are hindered from performing
frequently repeated therapies needed for effective recovery. For example, a stroke patient may be required to carry out
300 repetitions of any movement per day; however, most can only manage about 50 repetitions.
surgical attire, standing around a patient and operating a different robotic surgical system, with multiple articulated arms and
display screens showing the ongoing procedure.
Surgery with robot: surgical procedure 5000 km away using 5G technology.
Figure 3.12
Alt-Text - Short Description: Collage of four photos showing robotic devices assisting individuals in physical therapy
sessions.
Alt-Text - Long Description: The image is a collage of four separate photographs, each depicting a scene of robotic-assisted
physical therapy. In the first photo, an individual is lying on a therapy bed with a robotic arm positioned over their leg. The
second photo shows another individual on a similar bed, with a therapist adjusting a robotic arm that is interacting with the
person's leg. The third photo features a therapist and a patient, both interacting with a robotic arm during a session. The
fourth photo shows a therapist operating a robotic arm that is assisting a patient lying on a therapy bed.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Robotics rehabilitation targets the use of robotic technologies to aid in motor therapy of the patient after impairments,
such as stroke, as well as developing aids to support independent life for disabled and elderly people. Recent advances
increasingly involve cognitive aspects of motor control, using brain imaging technologies in making a feedback loop
from the brain to action. Robotics is pivotal in neuro-motor rehabilitation due to the flexible, programmable tools it
presents in making quantitative assessments and treatments possible. Besides motor rehabilitation, robotics also helps
with the mental well-being of seniors. Rapid progressions in assistive technologies for the disabled and elderly are now
merging human-centered design with integrated robotic and micro-mechatronic systems.
It was developed by Life Science Robotics of Denmark and is here now, in partnership with Innovative Rehab
Technologies Queensland start-up. ROBERT is one such mobile robot designed specifically for rehabilitation purposes:
it tackles both the upper and lower limbs, as well as assisting with recovery for patients undergoing neurorehabilitation,
and is currently in pilot stage with a major Queensland hospital. There is a severe shortage of rehabilitation therapists in
Australia, especially in the regional and remote parts of the country. In these areas, access to high-quality, intensive
therapy remains severely limited shown in Fig. 3.12. This lack of it means that patients are hindered from performing
frequently repeated therapies needed for effective recovery. For example, a stroke patient may be required to carry out
300 repetitions of any movement per day; however, most can only manage about 50 repetitions.
surgical attire, standing around a patient and operating a different robotic surgical system, with multiple articulated arms and
display screens showing the ongoing procedure.
Surgery with robot: surgical procedure 5000 km away using 5G technology.
Figure 3.12
Alt-Text - Short Description: Collage of four photos showing robotic devices assisting individuals in physical therapy
sessions.
Alt-Text - Long Description: The image is a collage of four separate photographs, each depicting a scene of robotic-assisted
physical therapy. In the first photo, an individual is lying on a therapy bed with a robotic arm positioned over their leg. The
second photo shows another individual on a similar bed, with a therapist adjusting a robotic arm that is interacting with the
person's leg. The third photo features a therapist and a patient, both interacting with a robotic arm during a session. The
fourth photo shows a therapist operating a robotic arm that is assisting a patient lying on a therapy bed.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
Founded by Dan Carter of Innovative Rehab Technologies, he states, “High dose, intense therapy must occur for the
patient to be able to regain movement.” ROBERT was designed to fill this gap: The system provides hundreds of
controlled repetitions per day-class of treatment that would be almost impossible to provide otherwise through manual
care. This technology has an application in the form of ROBERT, a mobile rehabilitation robot with which the clinician
can make use of for high-repetition therapy: it's critical in neurological rehabilitation and lessening physical
deconditioning happening throughout a hospital stay considering it can be moved freely in hospital wards, rehabilitation
centers, or outpatient clinics for quite a versatile delivery of consistent therapy in different healthcare settings (Fig. 3.12
).
Set-up ease also means allied health assistants and nurses can use the device as well, freeing up more hours in the day
for the physiotherapist and occupational therapists to see more patients. It is a flexible modality in terms of improving
workforce sustainability and efficiency under conditions of limited availability. Another feature unique to ROBERT is
the integration of EMG-triggered functional electrical stimulation. This can be used to start the therapy as early as
possible, even with minimal muscle activation. ROBERT will detect the small movements in muscles and activate the
relevant muscles, aiding in movement through predefined therapy paths. The Queensland hospital trial is an important
step in determining the value that ROBERT will have for healthcare service delivery. For a month, health service staff
will test the ability of ROBERT to support functional movement rehabilitation for patients and consider its place within
current therapy models used within the health service. The robot, after this trial, will move to another facility on the
Sunshine Coast for further testing. ROBERT is not a replacement for therapists; instead, it helps them through better
efficiency in treatment and additional patients being treated. By doing repetitive motion exercises, the time for therapists
to care for other patients is freed for better overall care delivery to be realized. Apart from the hospital trials, Innovative
Rehab Technologies was contacted by a leading global occupational therapy and rehabilitation research institution,
where they were approached to collaborate in further developing the upper limb software of ROBERT. This would
enable ROBERT to perform much better as a tool for neurorehabilitation treatment.
3.7.3 Case study 3: Microbots in drug delivery
In the vascular system, tracking devices that depend on optical visualization cannot be used for the follow-up of
microrobots because of the complexity of blood vessels and the absorbance of light by blood. This obstacle was
overcome by the imaging techniques with high resolution in spatiotemporal terms, such as ultrasound and MRI, which
have previously been applied for the tracking purposes of the movement of microrobots in vessels like the coronary
artery. Optical methods like photoacoustic imaging are confined to shallower tissues due to penetration problems. To
this end, MNPs-based biodegradable microrobots have been devised for the treatment of liver cancer by TACE (Fig.
3.13).
Robert rehabilitation robot. ROBERT will detect the small movements in muscles and activate the relevant muscles, aiding in
movement through predefined therapy paths.
Figure 3.13
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
patient to be able to regain movement.” ROBERT was designed to fill this gap: The system provides hundreds of
controlled repetitions per day-class of treatment that would be almost impossible to provide otherwise through manual
care. This technology has an application in the form of ROBERT, a mobile rehabilitation robot with which the clinician
can make use of for high-repetition therapy: it's critical in neurological rehabilitation and lessening physical
deconditioning happening throughout a hospital stay considering it can be moved freely in hospital wards, rehabilitation
centers, or outpatient clinics for quite a versatile delivery of consistent therapy in different healthcare settings (Fig. 3.12
).
Set-up ease also means allied health assistants and nurses can use the device as well, freeing up more hours in the day
for the physiotherapist and occupational therapists to see more patients. It is a flexible modality in terms of improving
workforce sustainability and efficiency under conditions of limited availability. Another feature unique to ROBERT is
the integration of EMG-triggered functional electrical stimulation. This can be used to start the therapy as early as
possible, even with minimal muscle activation. ROBERT will detect the small movements in muscles and activate the
relevant muscles, aiding in movement through predefined therapy paths. The Queensland hospital trial is an important
step in determining the value that ROBERT will have for healthcare service delivery. For a month, health service staff
will test the ability of ROBERT to support functional movement rehabilitation for patients and consider its place within
current therapy models used within the health service. The robot, after this trial, will move to another facility on the
Sunshine Coast for further testing. ROBERT is not a replacement for therapists; instead, it helps them through better
efficiency in treatment and additional patients being treated. By doing repetitive motion exercises, the time for therapists
to care for other patients is freed for better overall care delivery to be realized. Apart from the hospital trials, Innovative
Rehab Technologies was contacted by a leading global occupational therapy and rehabilitation research institution,
where they were approached to collaborate in further developing the upper limb software of ROBERT. This would
enable ROBERT to perform much better as a tool for neurorehabilitation treatment.
3.7.3 Case study 3: Microbots in drug delivery
In the vascular system, tracking devices that depend on optical visualization cannot be used for the follow-up of
microrobots because of the complexity of blood vessels and the absorbance of light by blood. This obstacle was
overcome by the imaging techniques with high resolution in spatiotemporal terms, such as ultrasound and MRI, which
have previously been applied for the tracking purposes of the movement of microrobots in vessels like the coronary
artery. Optical methods like photoacoustic imaging are confined to shallower tissues due to penetration problems. To
this end, MNPs-based biodegradable microrobots have been devised for the treatment of liver cancer by TACE (Fig.
3.13).
Robert rehabilitation robot. ROBERT will detect the small movements in muscles and activate the relevant muscles, aiding in
movement through predefined therapy paths.
Figure 3.13
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
These magnetically guided micro-robots implant therapeutic agents at tumor sites in the liver by blocking the feeding
vessels, as shown in Fig. 3.13. The micro-robot system, whereby a catheter releases therapeutic-agent-encapsulated
microbeads and MNPs, offers both real-time and postoperative imaging with MRI. This microrobot system allows more
precise targeting of liver tumors with external magnetic fields for navigation and could lead to more effective
chemoembolization therapy, where less damage would be induced to healthy tissues than in current methods.
3.8 Challenges in implementing smart robots in healthcare
Making healthcare robots acceptable has several challenges, particularly with respect to the elderly. The robots'
perceived value, dependability, enjoyment, and accessibility determine their adoption rate in great part. Complexity,
inadequate self-efficacy, and distrust add more difficulties to acceptance. Throughout the design process, developers
should include older persons to ensure the robots are suitable for their needs and appealing. The ability of the elderly to
operate appliances, get prescription reminders, and stop falls comes out as being quite beneficial. Finally, healthcare
robots supporting respect of the elderly and autonomy serve to raise social acceptability and overall well-being (
Darwish et al., 2019; Goyal et al., 2020).
3.8.1 Ethical and legal issues
The increasing application of smart robots in healthcare raises moral and legal questions. A big ethical question is
robotic autonomy, particularly in medication delivery and surgery. Medical robots raise questions about responsibility.
Should a robot execute surgery or administer the incorrect amount of medication, who bears liability, the manufacturer,
the hospital, or the clinician? Ambiguous responsibility must be proven in such situations before general application.
Informed consent raises another significant ethical issue. Patients must understand how robotics is involved in their
therapy, but explaining difficult technology to them is challenging. This issue is especially important as AI-driven
autonomous systems could not always tell healthcare professionals their judgments. Transparency and data ownership
bring moral questions. Smart robots cannot operate without enough patient data. However, must address questions
about who owns, uses, and safeguards this data. Many nations' legal frameworks still lag technological developments,
which leaves monitoring and control gaps (Navaz et al., 2021).
3.8.2 Safety, security, and privacy concerns
Alt-Text - Short Description: Illustration of magnetic actuation model for targeting tumors in the human body.
Alt-Text - Long Description: The image shows an illustration of a magnetic actuation model for targeting tumors in the
human body. On the left, there is a human figure with internal organs visible, highlighting a tumor in the chest area. A
magnetic device is shown targeting the tumor. On the right, there is a diagram of the intestines with labels indicating flow
direction, catheter approach and vessel blocking, tumor, drug release and targeting by magnetic guidance. There is a close-up
illustration of a device targeting a tumor within a blood vessel.
Schematic of targeted vessel embolization. These magnetically guided micro-robots implant therapeutic agents at tumor sites in the
liver by blocking the feeding vessels. In schematic of targeted vessel embolization and drug delivery using the microrobot system.
Injecting microscopic robots into the bloodstream. The magnetic fields guide the microrobots to the tumor site. After meeting the
tumor, the microrobots release their payload. The payload of the drug makes a direct attack on the cancer cells only, which reduces
damage to healthy tissue.
vessels, as shown in Fig. 3.13. The micro-robot system, whereby a catheter releases therapeutic-agent-encapsulated
microbeads and MNPs, offers both real-time and postoperative imaging with MRI. This microrobot system allows more
precise targeting of liver tumors with external magnetic fields for navigation and could lead to more effective
chemoembolization therapy, where less damage would be induced to healthy tissues than in current methods.
3.8 Challenges in implementing smart robots in healthcare
Making healthcare robots acceptable has several challenges, particularly with respect to the elderly. The robots'
perceived value, dependability, enjoyment, and accessibility determine their adoption rate in great part. Complexity,
inadequate self-efficacy, and distrust add more difficulties to acceptance. Throughout the design process, developers
should include older persons to ensure the robots are suitable for their needs and appealing. The ability of the elderly to
operate appliances, get prescription reminders, and stop falls comes out as being quite beneficial. Finally, healthcare
robots supporting respect of the elderly and autonomy serve to raise social acceptability and overall well-being (
Darwish et al., 2019; Goyal et al., 2020).
3.8.1 Ethical and legal issues
The increasing application of smart robots in healthcare raises moral and legal questions. A big ethical question is
robotic autonomy, particularly in medication delivery and surgery. Medical robots raise questions about responsibility.
Should a robot execute surgery or administer the incorrect amount of medication, who bears liability, the manufacturer,
the hospital, or the clinician? Ambiguous responsibility must be proven in such situations before general application.
Informed consent raises another significant ethical issue. Patients must understand how robotics is involved in their
therapy, but explaining difficult technology to them is challenging. This issue is especially important as AI-driven
autonomous systems could not always tell healthcare professionals their judgments. Transparency and data ownership
bring moral questions. Smart robots cannot operate without enough patient data. However, must address questions
about who owns, uses, and safeguards this data. Many nations' legal frameworks still lag technological developments,
which leaves monitoring and control gaps (Navaz et al., 2021).
3.8.2 Safety, security, and privacy concerns
Alt-Text - Short Description: Illustration of magnetic actuation model for targeting tumors in the human body.
Alt-Text - Long Description: The image shows an illustration of a magnetic actuation model for targeting tumors in the
human body. On the left, there is a human figure with internal organs visible, highlighting a tumor in the chest area. A
magnetic device is shown targeting the tumor. On the right, there is a diagram of the intestines with labels indicating flow
direction, catheter approach and vessel blocking, tumor, drug release and targeting by magnetic guidance. There is a close-up
illustration of a device targeting a tumor within a blood vessel.
Schematic of targeted vessel embolization. These magnetically guided micro-robots implant therapeutic agents at tumor sites in the
liver by blocking the feeding vessels. In schematic of targeted vessel embolization and drug delivery using the microrobot system.
Injecting microscopic robots into the bloodstream. The magnetic fields guide the microrobots to the tumor site. After meeting the
tumor, the microrobots release their payload. The payload of the drug makes a direct attack on the cancer cells only, which reduces
damage to healthy tissue.
Smart robots in healthcare create privacy, security, and safety hazards that must be addressed if we are to protect
institutions and people. One big problem is robotic system cybersecurity. Many smart robots are hackable, as many of
them are linked to other devices and hospital systems. A cyberattack on a medical robot might compromise patient
safety by upsetting patient monitoring or surgery (Adame et al., 2018). These systems must be kept free from
cyberattacks absolutely. The safety of patients undergoing robot-assisted operations is another problem. Robotic
devices must be tested for dependability and safety before they find use in healthcare environments. Mechanical
problems, improper software upgrades, and system faults all might endanger patients. Robots used in surgery or
rehabilitation that physically interact with humans must rigorously follow safety precautions to avoid injury. Privacy is
also quite important. Among the abundance of sensitive data, smart robots follow or diagnose patients acquire medical
history, real-time health measurements, and even personal interactions for social robots (Tunc et al., 2021).
3.8.3 Social acceptance and bias
The implementation of clever robots in healthcare is also contingent upon their social acceptability. Patients and
physicians may experience discomfort or distrust toward machines, as healthcare is a highly personal experience.
Patients may experience discomfort during robotic interventions, particularly those that necessitate human interaction,
such as surgery or physical therapy, according to research. Openness in the operation of autonomous systems and
education to demonstrate their benefits is essential for fostering confidence in them. Furthermore, the adoption of
robotics may be influenced by cultural disparities in technology perspectives. The Japanese population is more
receptive to the use of robotics in healthcare than the population of other countries due to the greater integration of
robotic technology into daily life. Bias is an additional concern. As with other AI-driven systems, biases in training data
can be reflected in clever robotics algorithms. This could result in robotics making unintentional judgments based on
race, gender, or socioeconomic status that are discriminatory or unfair to specific patient groups. Diagnostic robots may
exhibit underperformance when treating minority patients if their training data fails to accurately represent these
communities. Prejudice must be prevented by educating AI systems on a variety of datasets and subjecting them to
continuous evaluation.
3.8.4 Cost and accessibility issues
Especially in low- and middle-income countries, the great expense of smart robots makes adoption difficult. Robotic
systems ranging from surgical to rehabilitative to microbots call for significant upfront training and equipment costs.
Costing $1.5 to $2 million, including maintenance and disposable tools, the da Vinci Surgical System is among the
most often used surgical robots. For smaller hospitals and medical institutions with tighter budgets, these expenses
might be outrageous. Rich countries' healthcare systems have to strike a compromise between the cost-benefit of
robotics initiatives. Although robotic technologies might improve patient outcomes, recovery times, and complication
rates, financial savings cannot be shown right away. Usually reimbursed by insurance, robotic procedures may restrict
patient access to new technologies depending on this fact. The accessibility gap aggravates the financial load.
Underprivileged and rural patients might not have access to robotic-equipped facilities, therefore aggravating healthcare
disparity. Robotics must be made more affordable and available to many different groups if we are to transform
healthcare.
3.9 Future directions in smart robotics in healthcare
Smart robotics in health care promises a great deal, reflecting innovations that will rock patient care and medical
procedures as well. Several important trends and developments are now emerging to chart the future direction of this
field. Section this on the way forward as technology advances into more autonomous and assistive systems, AI and ML
integration, human-robot collaboration, and the digital twin concept.
3.9.1 Autonomous versus assistive systems
One of the biggest areas of growth in smart robotics is the distinction between autonomous and assistive systems.
Autonomous systems operate with little to no human intervention, and assistive systems work in concert with people to
augment their capabilities. Autonomous robots are expected to play an increasingly crucial role in healthcare. These
systems could potentially perform sophisticated surgeries, take care of patients in ICUs, and carry out emergency
interventions with minimal human supervision. With improved algorithms, robots are better positioned to make real-
time decisions based on vast datasets and present quicker, more accurate solutions in critical situations. For instance,
institutions and people. One big problem is robotic system cybersecurity. Many smart robots are hackable, as many of
them are linked to other devices and hospital systems. A cyberattack on a medical robot might compromise patient
safety by upsetting patient monitoring or surgery (Adame et al., 2018). These systems must be kept free from
cyberattacks absolutely. The safety of patients undergoing robot-assisted operations is another problem. Robotic
devices must be tested for dependability and safety before they find use in healthcare environments. Mechanical
problems, improper software upgrades, and system faults all might endanger patients. Robots used in surgery or
rehabilitation that physically interact with humans must rigorously follow safety precautions to avoid injury. Privacy is
also quite important. Among the abundance of sensitive data, smart robots follow or diagnose patients acquire medical
history, real-time health measurements, and even personal interactions for social robots (Tunc et al., 2021).
3.8.3 Social acceptance and bias
The implementation of clever robots in healthcare is also contingent upon their social acceptability. Patients and
physicians may experience discomfort or distrust toward machines, as healthcare is a highly personal experience.
Patients may experience discomfort during robotic interventions, particularly those that necessitate human interaction,
such as surgery or physical therapy, according to research. Openness in the operation of autonomous systems and
education to demonstrate their benefits is essential for fostering confidence in them. Furthermore, the adoption of
robotics may be influenced by cultural disparities in technology perspectives. The Japanese population is more
receptive to the use of robotics in healthcare than the population of other countries due to the greater integration of
robotic technology into daily life. Bias is an additional concern. As with other AI-driven systems, biases in training data
can be reflected in clever robotics algorithms. This could result in robotics making unintentional judgments based on
race, gender, or socioeconomic status that are discriminatory or unfair to specific patient groups. Diagnostic robots may
exhibit underperformance when treating minority patients if their training data fails to accurately represent these
communities. Prejudice must be prevented by educating AI systems on a variety of datasets and subjecting them to
continuous evaluation.
3.8.4 Cost and accessibility issues
Especially in low- and middle-income countries, the great expense of smart robots makes adoption difficult. Robotic
systems ranging from surgical to rehabilitative to microbots call for significant upfront training and equipment costs.
Costing $1.5 to $2 million, including maintenance and disposable tools, the da Vinci Surgical System is among the
most often used surgical robots. For smaller hospitals and medical institutions with tighter budgets, these expenses
might be outrageous. Rich countries' healthcare systems have to strike a compromise between the cost-benefit of
robotics initiatives. Although robotic technologies might improve patient outcomes, recovery times, and complication
rates, financial savings cannot be shown right away. Usually reimbursed by insurance, robotic procedures may restrict
patient access to new technologies depending on this fact. The accessibility gap aggravates the financial load.
Underprivileged and rural patients might not have access to robotic-equipped facilities, therefore aggravating healthcare
disparity. Robotics must be made more affordable and available to many different groups if we are to transform
healthcare.
3.9 Future directions in smart robotics in healthcare
Smart robotics in health care promises a great deal, reflecting innovations that will rock patient care and medical
procedures as well. Several important trends and developments are now emerging to chart the future direction of this
field. Section this on the way forward as technology advances into more autonomous and assistive systems, AI and ML
integration, human-robot collaboration, and the digital twin concept.
3.9.1 Autonomous versus assistive systems
One of the biggest areas of growth in smart robotics is the distinction between autonomous and assistive systems.
Autonomous systems operate with little to no human intervention, and assistive systems work in concert with people to
augment their capabilities. Autonomous robots are expected to play an increasingly crucial role in healthcare. These
systems could potentially perform sophisticated surgeries, take care of patients in ICUs, and carry out emergency
interventions with minimal human supervision. With improved algorithms, robots are better positioned to make real-
time decisions based on vast datasets and present quicker, more accurate solutions in critical situations. For instance,
future surgical robots can be capable of performing parts of even highly repetitive tasks with very high precision, like
suturing or tissue manipulation, while constantly monitoring the patient's vital signs autonomously. However, fully
autonomous systems in healthcare have a set of unique challenges in terms of problems due to trust, liability, and
regulatory approval. Such systems should be at least proven first and foremost to be safe and effective in a broad set of
unpredictable medical scenarios.
Assistive robots will probably dominate the near future of smart robotics in healthcare. The systems will continue to
enhance the abilities of medical practitioners by aiding them in executing complex tasks. For example, increased
dexterity and precision can be seen in surgery during the use of the surgical robot. Similarly, rehabilitation robots can
assist therapists in treatment procedures. These systems may reduce cognitive load and physical strain on healthcare
professionals while allowing attention to patient care and outcomes. As we proceed, the interaction between
autonomously controlled and assertively controlled systems will be crucial. Many experts believe that the future
belongs to hybrid systems that combine the strengths of both autonomous responsibility with the reliance on human
oversight for decisions in complicated or unpredictable situations (Silvera-Tawil, 2024).
3.9.2 Integration with AI and machine learning
It is believed that combining AI and ML with smart robotics will revolutionize healthcare when it opens the human
mind to new levels of intelligence and adaptability in robotic systems. This is likely to place AI in a position to process
massive amounts of medical data, recognize patterns, and learn from them so it can give more accurate diagnostics and
perhaps even better patient care. In surgical robotics, AI would potentially allow robots to learn from historical data
about how surgical procedures are performed and thereby get even better with time. For instance, ML algorithms might
scan hundreds of thousands of prior surgeries to produce models of optimal outcomes in real-time to guide the robot on
precise, patient-specific decisions. AI-based predictive analytics will further transform patient monitoring. AI-enablers
in robots can monitor the patients' vital signs and health status continuously, detect potential complications when they
are most likely to happen, and thus alert health care providers to intervene in good time. This would be very helpful,
especially in ICU and emergency applications, where it is literally a matter of life and death, upon which timely
interventions would mean all the difference. Another advantage of the adoption of AI is in the production of
customized rehabilitation programs. Robots can alter therapy based on the actual patient's performance in real time.
Thus, for example, during physiotherapy, AI-equipped robots may change exercise options considering the evolving
needs of the patient for better healing.
3.9.3 Human–robot collaboration
This will most likely be the future of health robotics: mixed, collaborative interaction between robots and humans, often
called cobots or collaborative robots. Such a system is meant to work directly with healthcare providers and
complement human capabilities where needed. The purpose of collaboration is to produce synergistic workflows where
robots take on repetitive, labor-intensive, or precise tasks, thereby liberating the health worker to interact with patients
and determine their decisions. For instance, in a surgical workflow, the human surgeon could oversee the procedure
while the robot performs intricate motions. In the same way, in rehabilitation applications, robots can assist physical
therapists in doing some mechanical but long-endurance motions that require consistency. Another area of focus is on
Human-Robot Interfaces (HRI) for future development. While designed for cooperative applications, such interfaces
need to be intuitive and user-friendly for healthcare providers to control and communicate with the robotic systems
effectively. Advances in natural language processing and gesture-based controls may allow healthcare workers to
command robots with simple voice commands or hand movements, making the process much more efficient and
having a lower learning curve. Another area of focus would be emotional intelligence in robots. Health care, especially
in the realms of eldercare and pediatric care, will require more robots that can interact with humans, pick up their
emotions, and respond accordingly for better patient outcomes and comfort.
3.9.4 Digital twin of robotics in healthcare
The use of digital twins, which are replicas of physical systems, is growing in the medical and robotics industries. The
digital twin has great diagnostic and therapy possibilities. A digital twin would be a real-time virtual representation of a
robot, together with its interactions and surroundings. The computer model could replicate actions, evaluate
performance, and project outcomes. Digital twins in healthcare help to enhance training and surgical planning. A digital
doppelganger of a surgical robot can be used by a surgeon to replicate a difficult operation. Entering patient-specific
suturing or tissue manipulation, while constantly monitoring the patient's vital signs autonomously. However, fully
autonomous systems in healthcare have a set of unique challenges in terms of problems due to trust, liability, and
regulatory approval. Such systems should be at least proven first and foremost to be safe and effective in a broad set of
unpredictable medical scenarios.
Assistive robots will probably dominate the near future of smart robotics in healthcare. The systems will continue to
enhance the abilities of medical practitioners by aiding them in executing complex tasks. For example, increased
dexterity and precision can be seen in surgery during the use of the surgical robot. Similarly, rehabilitation robots can
assist therapists in treatment procedures. These systems may reduce cognitive load and physical strain on healthcare
professionals while allowing attention to patient care and outcomes. As we proceed, the interaction between
autonomously controlled and assertively controlled systems will be crucial. Many experts believe that the future
belongs to hybrid systems that combine the strengths of both autonomous responsibility with the reliance on human
oversight for decisions in complicated or unpredictable situations (Silvera-Tawil, 2024).
3.9.2 Integration with AI and machine learning
It is believed that combining AI and ML with smart robotics will revolutionize healthcare when it opens the human
mind to new levels of intelligence and adaptability in robotic systems. This is likely to place AI in a position to process
massive amounts of medical data, recognize patterns, and learn from them so it can give more accurate diagnostics and
perhaps even better patient care. In surgical robotics, AI would potentially allow robots to learn from historical data
about how surgical procedures are performed and thereby get even better with time. For instance, ML algorithms might
scan hundreds of thousands of prior surgeries to produce models of optimal outcomes in real-time to guide the robot on
precise, patient-specific decisions. AI-based predictive analytics will further transform patient monitoring. AI-enablers
in robots can monitor the patients' vital signs and health status continuously, detect potential complications when they
are most likely to happen, and thus alert health care providers to intervene in good time. This would be very helpful,
especially in ICU and emergency applications, where it is literally a matter of life and death, upon which timely
interventions would mean all the difference. Another advantage of the adoption of AI is in the production of
customized rehabilitation programs. Robots can alter therapy based on the actual patient's performance in real time.
Thus, for example, during physiotherapy, AI-equipped robots may change exercise options considering the evolving
needs of the patient for better healing.
3.9.3 Human–robot collaboration
This will most likely be the future of health robotics: mixed, collaborative interaction between robots and humans, often
called cobots or collaborative robots. Such a system is meant to work directly with healthcare providers and
complement human capabilities where needed. The purpose of collaboration is to produce synergistic workflows where
robots take on repetitive, labor-intensive, or precise tasks, thereby liberating the health worker to interact with patients
and determine their decisions. For instance, in a surgical workflow, the human surgeon could oversee the procedure
while the robot performs intricate motions. In the same way, in rehabilitation applications, robots can assist physical
therapists in doing some mechanical but long-endurance motions that require consistency. Another area of focus is on
Human-Robot Interfaces (HRI) for future development. While designed for cooperative applications, such interfaces
need to be intuitive and user-friendly for healthcare providers to control and communicate with the robotic systems
effectively. Advances in natural language processing and gesture-based controls may allow healthcare workers to
command robots with simple voice commands or hand movements, making the process much more efficient and
having a lower learning curve. Another area of focus would be emotional intelligence in robots. Health care, especially
in the realms of eldercare and pediatric care, will require more robots that can interact with humans, pick up their
emotions, and respond accordingly for better patient outcomes and comfort.
3.9.4 Digital twin of robotics in healthcare
The use of digital twins, which are replicas of physical systems, is growing in the medical and robotics industries. The
digital twin has great diagnostic and therapy possibilities. A digital twin would be a real-time virtual representation of a
robot, together with its interactions and surroundings. The computer model could replicate actions, evaluate
performance, and project outcomes. Digital twins in healthcare help to enhance training and surgical planning. A digital
doppelganger of a surgical robot can be used by a surgeon to replicate a difficult operation. Entering patient-specific
data like medical images or vital statistics, the surgeon might test the robot in a simulated environment, seeing any
problems and adjusting the surgical plan. Digital twins’ predictive maintenance might help hospital robotic systems
shown in Fig. 3.14 (Sosa-Méndez and García Cena, 2023). Constantly monitoring the physical robot's performance,
the twin would find defects before they resulted in mechanical breakdowns or medical errors. This would prevent
downtime and maintain robotic systems for patient treatment. By aggregating patient data to enable the robot to meet
patient needs, digital twins may also assist in tailoring treatment. The digital twin could represent patient growth and
suggest robotic system changes to enhance recovery in rehabilitation (Yaacoub et al., 2023). Digital twins might hasten
research-based robotic technology development and testing. Multiple computer simulations allow engineers to identify
the most interesting ideas and methods before they are tested in real life, therefore accelerating innovation and
enhancing outcomes (Augustine, 2020; Katsoulakis et al., 2024; Fig. 3.14).
3.10 Conclusion
Intelligent robots used in the industry are a novel finding with tremendous promise to enhance patient care, decrease
administrative workloads, and assist healthcare staff. Two key industrial developments resulting from technological
advancements are mobile robots, which aid in patient care and logistics, and surgical robotics, which improve precision
and eliminate the possibility of human mistakes. Two examples demonstrate the versatility and adaptability of smart
robots in the healthcare industry: microbots for drug delivery and rehabilitation robotics. The combination of AI, ML,
and robotics will increasingly define healthcare robots in the future. This convergence will allow systems to transition
from a supporting role to that of semi- or totally autonomous beings. When paired with human healthcare practitioners,
these technologies may improve outcomes by offering personalized treatment plans, shortening recovery periods, and
enhancing productivity in areas with limited resources. Real-world healthcare environments continue to pose
insurmountable challenges for robotics initiatives. Ethical norms, privacy, safety, societal acceptability, and economic
Figure 3.14
Alt-Text - Short Description: An infographic showing the process of diagnosis and treatment using physical and virtual
entities.
Alt-Text - Long Description: The infographic illustrates the process of diagnosis and treatment using physical and virtual
entities. On the left side, it shows 'Diagnosis and Treatment' with 'Physical Entity' and 'Mobile Health'. Below this, it lists
various multi-omics approaches including 'Transcriptomics', 'Proteomics', 'Metabolomics', 'Lipidomics', 'Epigenomics',
'Interactomics', 'Phenomics', 'Genomics' and 'Ionomics'. On the right side, it shows 'Modelling & Simulation' with 'Virtual
Entity'. Below this, it lists 'Optimal treatment strategy', 'Health Trajectory prediction' and 'Virtual clinical trials'.
Digital twin for health (DT4H) envisioned. Digital twins’ predictive maintenance might help hospital robotic system. It explains how
genomes, transcriptomics, metabolomics, and phenomics may be utilized to build a digital twin of a patient. Modelling and
Simulation: The digital twin can optimize treatment approach by simulating all therapies and how different interventions influence
them. Health Trajectory Prediction: The digital twin can anticipate future outcomes based on a patient's data and historical patterns,
enabling more timely treatments and prevention. Virtual Clinical Trials: Digital twins can test novel medications and therapies in a
virtual setting, reducing clinical trials and speeding drug development.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
problems and adjusting the surgical plan. Digital twins’ predictive maintenance might help hospital robotic systems
shown in Fig. 3.14 (Sosa-Méndez and García Cena, 2023). Constantly monitoring the physical robot's performance,
the twin would find defects before they resulted in mechanical breakdowns or medical errors. This would prevent
downtime and maintain robotic systems for patient treatment. By aggregating patient data to enable the robot to meet
patient needs, digital twins may also assist in tailoring treatment. The digital twin could represent patient growth and
suggest robotic system changes to enhance recovery in rehabilitation (Yaacoub et al., 2023). Digital twins might hasten
research-based robotic technology development and testing. Multiple computer simulations allow engineers to identify
the most interesting ideas and methods before they are tested in real life, therefore accelerating innovation and
enhancing outcomes (Augustine, 2020; Katsoulakis et al., 2024; Fig. 3.14).
3.10 Conclusion
Intelligent robots used in the industry are a novel finding with tremendous promise to enhance patient care, decrease
administrative workloads, and assist healthcare staff. Two key industrial developments resulting from technological
advancements are mobile robots, which aid in patient care and logistics, and surgical robotics, which improve precision
and eliminate the possibility of human mistakes. Two examples demonstrate the versatility and adaptability of smart
robots in the healthcare industry: microbots for drug delivery and rehabilitation robotics. The combination of AI, ML,
and robotics will increasingly define healthcare robots in the future. This convergence will allow systems to transition
from a supporting role to that of semi- or totally autonomous beings. When paired with human healthcare practitioners,
these technologies may improve outcomes by offering personalized treatment plans, shortening recovery periods, and
enhancing productivity in areas with limited resources. Real-world healthcare environments continue to pose
insurmountable challenges for robotics initiatives. Ethical norms, privacy, safety, societal acceptability, and economic
Figure 3.14
Alt-Text - Short Description: An infographic showing the process of diagnosis and treatment using physical and virtual
entities.
Alt-Text - Long Description: The infographic illustrates the process of diagnosis and treatment using physical and virtual
entities. On the left side, it shows 'Diagnosis and Treatment' with 'Physical Entity' and 'Mobile Health'. Below this, it lists
various multi-omics approaches including 'Transcriptomics', 'Proteomics', 'Metabolomics', 'Lipidomics', 'Epigenomics',
'Interactomics', 'Phenomics', 'Genomics' and 'Ionomics'. On the right side, it shows 'Modelling & Simulation' with 'Virtual
Entity'. Below this, it lists 'Optimal treatment strategy', 'Health Trajectory prediction' and 'Virtual clinical trials'.
Digital twin for health (DT4H) envisioned. Digital twins’ predictive maintenance might help hospital robotic system. It explains how
genomes, transcriptomics, metabolomics, and phenomics may be utilized to build a digital twin of a patient. Modelling and
Simulation: The digital twin can optimize treatment approach by simulating all therapies and how different interventions influence
them. Health Trajectory Prediction: The digital twin can anticipate future outcomes based on a patient's data and historical patterns,
enabling more timely treatments and prevention. Virtual Clinical Trials: Digital twins can test novel medications and therapies in a
virtual setting, reducing clinical trials and speeding drug development.
i
Images may appear blurred during proofing as they have been optimized for fast web viewing. A high quality version
will be used in the final publication. Click on the image to view the original version.
considerations for incorporating many stakeholders are major challenges. Everyone, from public workers and
politicians to engineers and physicians, should contribute to this effort. To appropriately deploy smart robots, one must
weigh the potential risks against the likely benefits. Future breakthroughs in digital twin technology and human-robot
interaction might result in even more significant shifts in the business. Many diverse individuals rely on this technology
because healthcare systems across the world. As a result, the low cost and accessibility of these technologies should be
given top priority. As a result, smart robots will always be accessible to everyone, regardless of location or income
level.
References
i
The corrections made in this section will be reviewed and approved by the master copier.
Acharya, A.D., Patil, S.N., 2020. IoT based health care monitoring kit. In: 2020 Fourth International
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politicians to engineers and physicians, should contribute to this effort. To appropriately deploy smart robots, one must
weigh the potential risks against the likely benefits. Future breakthroughs in digital twin technology and human-robot
interaction might result in even more significant shifts in the business. Many diverse individuals rely on this technology
because healthcare systems across the world. As a result, the low cost and accessibility of these technologies should be
given top priority. As a result, smart robots will always be accessible to everyone, regardless of location or income
level.
References
i
The corrections made in this section will be reviewed and approved by the master copier.
Acharya, A.D., Patil, S.N., 2020. IoT based health care monitoring kit. In: 2020 Fourth International
Conference on Computing Methodologies and Communication (ICCMC). IEEE, pp. 363–368.
doi:10.1109/ICCMC48092.2020.ICCMC-00068.
Adame, T., Bel, A., Carreras, A., Melià-Seguí, J., Oliver, M., Pous, R., 2018. CUIDATS: an RFID–WSN
hybrid monitoring system for smart health care environments. Future Gener. Comput. Syst. 78, 602–615.
doi:10.1016/j.future.2016.12.023.
Adel, A., 2024. The convergence of intelligent tutoring, robotics, and iot in smart education for the transition
from industry 4.0 to 5.0. Smart Cities 7 (1), 325–369. doi:10.3390/smartcities7010014.
Afsana, F., Asif-Ur-Rahman, M., Ahmed, M.R., Mahmud, M., Kaiser, M.S, 2018. An energy conserving
routing scheme for wireless body sensor nanonetwork communication. IEEE Access 6, 9186–9200.
doi:10.1109/ACCESS.2018.2789437.
Agrawal, A., Soni, R., Gupta, D., Dubey, G., 2024. The role of robotics in medical science: advancements,
applications, and future directions. J. Aut. Intell. 7 (3). doi:10.32629/jai.v7i3.1008.
Ahmed, I., Jeon, G., Chehri, A., 2023. An IoT-enabled smart health care system for screening of COVID-19
with multilayers features fusion and selection. Computing 105 (4), 743–760. doi:10.1007/s00607-021-00992-
0.
Akpakwu, G.A., Silva, B.J., Hancke, G.P., Abu-Mahfouz, A.M., 2018. A survey on 5G networks for the
internet of things: communication technologies and challenges. IEEE Access 6, 3619–3647.
doi:10.1109/ACCESS.2017.2779844.
Al-kahtani, M.S., Khan, F., Taekeun, W., 2022. Application of Internet of Things and sensors in healthcare.
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Allam, K., 2022. Smart robotics: a deep exploration of big data integration abstract. EPH Int. J. Humanit.
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Allam, K., 2023. Data-driven dynamics: unraveling the potential of smart robotics in the age of big data. EPH
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023-00388-3.
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Ordoñez-Ordoñez, O.J., “Applied sciences smart healthcare applications over 5G networks 2023.
Porkodi, S., Kesavaraja, D., 2021. Healthcare robots enabled with IoT and artificial intelligence for elderly
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Pradhan, B., et al., 2021. Internet of things and robotics in transforming current-day healthcare services. J.
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Pransky, J., 2016. The Pransky interview: Dr Steve Cousins, CEO, savioke, entrepreneur and innovator. Ind.
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Remacle, M., Prasad, V.M.N., Lawson, G., Plisson, L., Bachy, V., Van der Vorst, S., 2015. Transoral robotic
surgery (TORS) with the Medrobotics Flex
TM
System: first surgical application on humans. Eur. Arch. Oto-
Rhino-Laryngol. 272 (6), 1451–1455. doi:10.1007/s00405-015-3532-x.
Roche, M., 2021. The MAKO robotic-arm knee arthroplasty system. Arch. Orthop. Trauma Surg. 141 (12),
2043–2047. doi:10.1007/s00402-021-04208-0.
Santos, N.B., Bavaresco, R.S., Tavares, J.E.R., de O. Ramos, G., Barbosa, J.L.V., 2021. A systematic
mapping study of robotics in human care. Rob, Aut., Syst 144, 103833. doi:10.1016/j.robot.2021.103833.
Sarker, S., Jamal, L., Ahmed, S.F., Irtisam, N., 2021. Robotics and artificial intelligence in healthcare during
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Selvi, S.K, Dey, S., Ramasamy, S.S., and Singh, K.V., “Nano robots promising advancements and challenges in
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Tulsulkar, G., Mishra, N., Thalmann, N.M., Lim, H.E., Lee, M.P., Cheng, S.K., 2021. Can a humanoid
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Robot. AI 9 (November), 1–17. doi:10.3389/frobt.2022.1027415.
Sharma, A., Malviya, R., Gupta, R., 2022. Big data analytics in healthcare. Cogn. Intell. Big Data Heal.
2015, 257–301. doi:10.1002/9781119771982.ch10.
Shafik, W., Tufail, A., Liyanage, C.D.S., and Apong, R.A.A.H.M., “Medical robotics and AI-assisted
diagnostics challenges for smart sustainable healthcare,” 2024, pp. 304–323. doi: 10.4018/979-8-3693-3218-
4.ch016.Q18
Sikdar, S. and Guha, S., Advancements of Healthcare Technologies: Paradigm Towards Smart Healthcare
Systems, vol. 1124. Springer, Singapore, 2020. doi: 10.1007/978-981-15-2740-1_9.
Silvera-Tawil, D., 2024. Robotics in healthcare: a survey. SN Comput. Sci. 5 (1). doi:10.1007/s42979-023-
02551-0.
Siripurapu, S., Darimireddy, N.K., Chehri, A., Sridhar, B., Paramkusam, A.V., 2023. Technological
advancements and elucidation gadgets for healthcare applications: an exhaustive methodological review-Part-
I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing). Electronics 12 (3).
doi:10.3390/electronics12030750.
Siriwardhana, Y., De Alwis, C., Gur, G., Ylianttila, M., Liyanage, M., 2020. The fight against the COVID-
19 pandemic with 5G technologies. IEEE Eng. Manag. Rev. 48 (3), 72–84.
doi:10.1109/EMR.2020.3017451.
Soljacic, F., Law, T., Chita-Tegmark, M., Scheutz, M., 2024. Robots in healthcare as envisioned by care
professionals. Intell. Robot 17 (3), 685–701. doi:10.1007/s11370-024-00523-8.
Sosa-Méndez, D., García Cena, C.E., 2023. Robotic digital twin as a training platform for rehabilitation
health personnel. Enfoque UTE 14 (3), 19–26. doi:10.29019/enfoqueute.971.
Tavakoli, M., Carriere, J., Torabi, A., 2020. Robotics, smart wearable technologies, and autonomous
intelligent systems for healthcare during the COVID-19 pandemic: an analysis of the state of the art and
future vision. Adv. Intell. Syst. 2 (7). doi:10.1002/aisy.202000071.
Tripathi, D., Khondakar, K.R., 2024. Biomedical soft robotics in healthcare. Next-Generation Smart
Biosensing. Elsevier, kolkata, pp. 229–260. doi:10.1016/B978-0-323-98805-6.00011-7.Q19
Tulsulkar, G., Mishra, N., Thalmann, N.M., Lim, H.E., Lee, M.P., Cheng, S.K., 2021. Can a humanoid
social robot stimulate the interactivity of cognitively impaired elderly? A thorough study based on computer
vision methods. Vis. Comput. 37 (12), 3019–3038. doi:10.1007/s00371-021-02242-y.
Tunc, M.A., Gures, E., and Shayea, I., “A survey on IoT Smart healthcare: emerging technologies,
applications, challenges, and future trends,” pp. 1–15, 2021. http://arxiv.org/abs/2109.02042.Q20
Verma, P., Tiwari, R., Hong, W.-C., Upadhyay, S., Yeh, Y.-H., 2022. FETCH: a deep learning-based fog
computing and iot integrated environment for healthcare monitoring and diagnosis. IEEE Access 10, 12548–
12563. doi:10.1109/ACCESS.2022.3143793.
Vieira, M.G, Rogerio, V., Roschel, P., Rabelo, V., Teixeira, T., Muñoz-Lora, V.R.M., 2022. Myomodulation
using hyaluronic acid fillers as an efficient and innovative treatment for gummy smile: a case report. J. Oral.
Biol. Craniofacial Res. 12 (3), 376–380. doi:10.1016/j.jobcr.2022.04.009.
Wan, S., Gu, Z., Ni, Q., 2020. Cognitive computing and wireless communications on the edge for healthcare
service robots. Comput. Commun. 149 (October 2019), 99–106. doi:10.1016/j.comcom.2019.10.012.
Wang, Y., Xie, Z., Huang, H., Liang, X., 2024. Pioneering healthcare with soft robotic devices: a review.
Smart Med. 3 (1). doi:10.1002/SMMD.20230045.
Yaacoub, J.P.A., Noura, H.N., Piranda, B., 2023. The internet of modular robotic things: issues, limitations,
challenges, & solutions. Internet of Things 23, 100886. doi:10.1016/j.iot.2023.100886.
Yang, C., Guo, S., Bao, X., 2022. An isomorphic interactive device for the interventional surgical robot after
in vivo study. Micromachines 13 (1). doi:10.3390/mi13010111.
Yang, D.M., et al., 2023. Smart healthcare: a prospective future medical approach for COVID-19. J. Chin.
Med. Assoc. 86 (2), 138–146. doi:10.1097/JCMA.0000000000000824.
Yang, G., et al., 2020. Homecare robotic systems for healthcare 4.0: visions and enabling technologies. IEEE
J. Biomed. Heal. Inform. 24 (9), 2535–2549. doi:10.1109/JBHI.2020.2990529.
Zhang, Z.S., Wu, Y., Zheng, B., 2024. A review of cognitive support systems in the operating room. Surg.
Innov. 31 (1), 111–122. doi:10.1177/15533506231218962. Abstract
The adaption of smart robotics in the healthcare domain is becoming increasingly popular. After coronavirus disease-2019, there has
been remarkable demand for robots, especially in the clinical sector. Collaborative mobile robots have also become excellent hospital
assistants, providing support to the frontline healthcare workers, operating rooms, intensive care units, and risky areas for the
healthcare team. The field of smart robotics are evolving with the advancements being made across the various applications like in
surgical robotics, where robots are becoming more precise and dexterous, allowing surgeons to perform intricate procedures, with
greater accuracy, rehabilitation helps patients adjusting their movements in real time. There are smart robots, such as social robots
that can interact with users or microbots that deliver drugs inside the body. These robotic systems collect data from different sensors
like visual, auditory, physical, or chemical. The robot's processor manipulates, analyzes, and interprets the data accordingly. The
actuators enable the robots to perform different functions, including visual, physical, auditory, or chemical responses. Smart robotic
systems can be grouped along an assistive-to-autonomous axis. By gathering and evaluating data, carrying out specific tasks under
human supervision (such operating a semiautonomous ultrasound scanner) or picking up skills from watching medical professionals
conduct them, assistive systems improve the capabilities of their users. For instance, a physiotherapist could teach a robot how to
assist a patient with repetitive rehabilitation activities. The current book chapter tries to explore various applications of smart
robotics in various healthcare sectors, including a few examples and use cases. Even though smart robotics in healthcare is the
subject of extensive research, implementation in actual healthcare settings is still quite low. We must address concerns including
safety, security, privacy, and ethical principles; identify and remove bias that can lead to injustice or harmful clinical choices; and
foster social acceptance of AI to remove adoption hurdles.
Keywords: Microbots; Mobile robots; Sensors; Smart robotics; Surgical robots; Wearable devices Queries and Answers
service robots. Comput. Commun. 149 (October 2019), 99–106. doi:10.1016/j.comcom.2019.10.012.
Wang, Y., Xie, Z., Huang, H., Liang, X., 2024. Pioneering healthcare with soft robotic devices: a review.
Smart Med. 3 (1). doi:10.1002/SMMD.20230045.
Yaacoub, J.P.A., Noura, H.N., Piranda, B., 2023. The internet of modular robotic things: issues, limitations,
challenges, & solutions. Internet of Things 23, 100886. doi:10.1016/j.iot.2023.100886.
Yang, C., Guo, S., Bao, X., 2022. An isomorphic interactive device for the interventional surgical robot after
in vivo study. Micromachines 13 (1). doi:10.3390/mi13010111.
Yang, D.M., et al., 2023. Smart healthcare: a prospective future medical approach for COVID-19. J. Chin.
Med. Assoc. 86 (2), 138–146. doi:10.1097/JCMA.0000000000000824.
Yang, G., et al., 2020. Homecare robotic systems for healthcare 4.0: visions and enabling technologies. IEEE
J. Biomed. Heal. Inform. 24 (9), 2535–2549. doi:10.1109/JBHI.2020.2990529.
Zhang, Z.S., Wu, Y., Zheng, B., 2024. A review of cognitive support systems in the operating room. Surg.
Innov. 31 (1), 111–122. doi:10.1177/15533506231218962. Abstract
The adaption of smart robotics in the healthcare domain is becoming increasingly popular. After coronavirus disease-2019, there has
been remarkable demand for robots, especially in the clinical sector. Collaborative mobile robots have also become excellent hospital
assistants, providing support to the frontline healthcare workers, operating rooms, intensive care units, and risky areas for the
healthcare team. The field of smart robotics are evolving with the advancements being made across the various applications like in
surgical robotics, where robots are becoming more precise and dexterous, allowing surgeons to perform intricate procedures, with
greater accuracy, rehabilitation helps patients adjusting their movements in real time. There are smart robots, such as social robots
that can interact with users or microbots that deliver drugs inside the body. These robotic systems collect data from different sensors
like visual, auditory, physical, or chemical. The robot's processor manipulates, analyzes, and interprets the data accordingly. The
actuators enable the robots to perform different functions, including visual, physical, auditory, or chemical responses. Smart robotic
systems can be grouped along an assistive-to-autonomous axis. By gathering and evaluating data, carrying out specific tasks under
human supervision (such operating a semiautonomous ultrasound scanner) or picking up skills from watching medical professionals
conduct them, assistive systems improve the capabilities of their users. For instance, a physiotherapist could teach a robot how to
assist a patient with repetitive rehabilitation activities. The current book chapter tries to explore various applications of smart
robotics in various healthcare sectors, including a few examples and use cases. Even though smart robotics in healthcare is the
subject of extensive research, implementation in actual healthcare settings is still quite low. We must address concerns including
safety, security, privacy, and ethical principles; identify and remove bias that can lead to injustice or harmful clinical choices; and
foster social acceptance of AI to remove adoption hurdles.
Keywords: Microbots; Mobile robots; Sensors; Smart robotics; Surgical robots; Wearable devices Queries and Answers
Q1
Query: Please check the chapter title and amend if necessary.
Answer: Yes
Q2
Query: Please verify and confirm the authorship details (author name and surname, affiliation, spelling of author’s name and
author’s order) and amend if necessary.
Answer: Yes
Q3
Query: Please check all figure captions as set for correctness.
Answer: yes its good
Q4
Query: Some text parts of Figs. 3.3, 3.5, 3.6, 3.9, 3.10, 3.13, and 3.14 have been extracted from the main text. Please check for
accuracy.
Answer: Corrections are made accordingly
Q5
Query: Please provide publisher location for Reference “Bohr and Memarzadeh, 2020”.
Answer: updated
Q6
Query: Please provide book title, publisher name, and publisher location for Ref. “Coradeschi et al., 2014”.
Answer: Human-Computer Systems Interaction: Backgrounds and Applications 3, Springer International Publishing, Swizerland
Q7
Query: Please check ref ``Gardner et al., 2017'' for correctness.
Answer: Auckland, New Zealand
Q8
Query: Please provide publisher location for Reference “Guntur et al., 2019”.
Answer: Kolkata
Query: Please check the chapter title and amend if necessary.
Answer: Yes
Q2
Query: Please verify and confirm the authorship details (author name and surname, affiliation, spelling of author’s name and
author’s order) and amend if necessary.
Answer: Yes
Q3
Query: Please check all figure captions as set for correctness.
Answer: yes its good
Q4
Query: Some text parts of Figs. 3.3, 3.5, 3.6, 3.9, 3.10, 3.13, and 3.14 have been extracted from the main text. Please check for
accuracy.
Answer: Corrections are made accordingly
Q5
Query: Please provide publisher location for Reference “Bohr and Memarzadeh, 2020”.
Answer: updated
Q6
Query: Please provide book title, publisher name, and publisher location for Ref. “Coradeschi et al., 2014”.
Answer: Human-Computer Systems Interaction: Backgrounds and Applications 3, Springer International Publishing, Swizerland
Q7
Query: Please check ref ``Gardner et al., 2017'' for correctness.
Answer: Auckland, New Zealand
Q8
Query: Please provide publisher location for Reference “Guntur et al., 2019”.
Answer: Kolkata
Q9
Query: Please provide complete bibliographic details for Reference “Iftikhar et al., 2000”.
Answer: Iftikhar, Muhammad MBBSa; Saqib, Muhammad MBBSa,*; Zareen, Muhammad MBBS, FCPS Surgery, FMAS,
MRCSa; Mumtaz, Hassan MBBS, MSPH, MRSPHb. Artificial intelligence: revolutionizing robotic surgery: review. Annals of
Medicine & Surgery 86(9):p 5401-5409, September 2024. | DOI: 10.1097/MS9.0000000000002426
Q10
Query: Please provide complete bibliographic details for Reference “James et al., 2024”.
Answer: Boqing Jia, Zhikang Li, Tengfei Zheng, Jiaxiang Wang, Zhi-Jun Zhao, Libo Zhao, Bin Wang, Jijian Lu, Kang Zhao,
Guoxi Luo, Min Li, Qijing Lin, Zhuangde Jiang,Highly-sensitive, broad-range, and highly-dynamic MXene pressure sensors
with multi-level nano-microstructures for healthcare and soft robots applications, Chemical Engineering Journal, Volume
485,2024,149750,ISSN 1385-8947, https://doi.org/10.1016/j.cej.2024.149750.
Q11
Query: Please provide publisher location for Reference “Karmakar et al., 2024”.
Answer: Kolkata
Q12
Query: Please provide complete bibliographic details for Reference “Kaur, 2024”.
Answer: Kaur, J. (2024). Synergistic Evolution: Pioneering Frontiers of Artificial Intelligence and Robotics in Healthcare. In:
Manoharan, S., Tugui, A., Baig, Z. (eds) Proceedings of 4th International Conference on Artificial Intelligence and Smart
Energy. ICAIS 2024. Information Systems Engineering and Management, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-
031-61475-0_12
Q13
Query: Please provide complete bibliographic details for Reference “Keshary et al., 2022”.
Answer: Keshary, S., Dharmaraj, G., Balasubramanian, S., Srinivasan, S. (2022). IoT-Based Personalized Health Care for
Elderly Diabetic Patients. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and
Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-
4016-2_40
Q14
Query: Please provide complete bibliographic details for Reference “Khang, 2024”.
Answer: Khang, A. (Ed.). (2024). AI and IoT Technology and Applications for Smart Healthcare Systems (1st ed.). Auerbach
Publications. https://doi.org/10.1201/9781032686745
Query: Please provide complete bibliographic details for Reference “Iftikhar et al., 2000”.
Answer: Iftikhar, Muhammad MBBSa; Saqib, Muhammad MBBSa,*; Zareen, Muhammad MBBS, FCPS Surgery, FMAS,
MRCSa; Mumtaz, Hassan MBBS, MSPH, MRSPHb. Artificial intelligence: revolutionizing robotic surgery: review. Annals of
Medicine & Surgery 86(9):p 5401-5409, September 2024. | DOI: 10.1097/MS9.0000000000002426
Q10
Query: Please provide complete bibliographic details for Reference “James et al., 2024”.
Answer: Boqing Jia, Zhikang Li, Tengfei Zheng, Jiaxiang Wang, Zhi-Jun Zhao, Libo Zhao, Bin Wang, Jijian Lu, Kang Zhao,
Guoxi Luo, Min Li, Qijing Lin, Zhuangde Jiang,Highly-sensitive, broad-range, and highly-dynamic MXene pressure sensors
with multi-level nano-microstructures for healthcare and soft robots applications, Chemical Engineering Journal, Volume
485,2024,149750,ISSN 1385-8947, https://doi.org/10.1016/j.cej.2024.149750.
Q11
Query: Please provide publisher location for Reference “Karmakar et al., 2024”.
Answer: Kolkata
Q12
Query: Please provide complete bibliographic details for Reference “Kaur, 2024”.
Answer: Kaur, J. (2024). Synergistic Evolution: Pioneering Frontiers of Artificial Intelligence and Robotics in Healthcare. In:
Manoharan, S., Tugui, A., Baig, Z. (eds) Proceedings of 4th International Conference on Artificial Intelligence and Smart
Energy. ICAIS 2024. Information Systems Engineering and Management, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-
031-61475-0_12
Q13
Query: Please provide complete bibliographic details for Reference “Keshary et al., 2022”.
Answer: Keshary, S., Dharmaraj, G., Balasubramanian, S., Srinivasan, S. (2022). IoT-Based Personalized Health Care for
Elderly Diabetic Patients. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and
Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-
4016-2_40
Q14
Query: Please provide complete bibliographic details for Reference “Khang, 2024”.
Answer: Khang, A. (Ed.). (2024). AI and IoT Technology and Applications for Smart Healthcare Systems (1st ed.). Auerbach
Publications. https://doi.org/10.1201/9781032686745
Q15
Query: Please provide publisher location for Reference “Kilby et al., 2020”.
Answer: Sunnyvale, CA, United States
Q16
Query: Please provide complete bibliographic details for Reference “Lalit et al., 2024”.
Answer: Lalit, V., Sharma, Y., Ekvitayavetchanukul, P., Majumder, J., Biswas, S., Gangopadhyay, S. (2024). Operational
Challenges in Modern Business Evolution in Healthcare Technology Startups. In: Kumar, P., Singh, P., Diwakar, M., Garg, D.
(eds) Healthcare Industry Assessment: Analyzing Risks, Security, and Reliability. Engineering Cyber-Physical Systems and
Critical Infrastructures, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-65434-3_13
Q17
Query: Please provide complete bibliographic details for Reference “Selvi et al., 2024”.
Answer: Selvi, S.K, Dey, S., Ramasamy, S.S., and Singh, K.V., “Nano robots promising advancements and challenges in
healthcare,” 2024, pp. 1–16.
Q18
Query: Please provide complete bibliographic details for Reference “Shafik et al., 2024”.
Answer: 10.4018/979-8-3693-3218-4.ch016
Q19
Query: Please provide publisher location for Reference “Tripathi and Khondakar, 2024”.
Answer: kolkata
Q20
Query: Please provide complete bibliographic details for Reference “Tunc et al., 2021”.
Answer: updated
Query: Please provide publisher location for Reference “Kilby et al., 2020”.
Answer: Sunnyvale, CA, United States
Q16
Query: Please provide complete bibliographic details for Reference “Lalit et al., 2024”.
Answer: Lalit, V., Sharma, Y., Ekvitayavetchanukul, P., Majumder, J., Biswas, S., Gangopadhyay, S. (2024). Operational
Challenges in Modern Business Evolution in Healthcare Technology Startups. In: Kumar, P., Singh, P., Diwakar, M., Garg, D.
(eds) Healthcare Industry Assessment: Analyzing Risks, Security, and Reliability. Engineering Cyber-Physical Systems and
Critical Infrastructures, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-65434-3_13
Q17
Query: Please provide complete bibliographic details for Reference “Selvi et al., 2024”.
Answer: Selvi, S.K, Dey, S., Ramasamy, S.S., and Singh, K.V., “Nano robots promising advancements and challenges in
healthcare,” 2024, pp. 1–16.
Q18
Query: Please provide complete bibliographic details for Reference “Shafik et al., 2024”.
Answer: 10.4018/979-8-3693-3218-4.ch016
Q19
Query: Please provide publisher location for Reference “Tripathi and Khondakar, 2024”.
Answer: kolkata
Q20
Query: Please provide complete bibliographic details for Reference “Tunc et al., 2021”.
Answer: updated
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