Healthcare AI Agents Playbook for Hospitals and Health tech
rishabhsood31
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35 slides
Oct 07, 2025
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About This Presentation
Healthcare AI is revolutionizing patient care and doctors' administrative burden. 2025 is a turning point for AI adoption in healthcare.
Modern hospitals and health systems are implementing agentic AI to enhance patient care, streamline workflows, reduce clinician burnout, and boost financial ...
Slide Content
A guide to AI assistants for
healthcare delivery
AI Agents in
Healthcare
healthcare delivery
AI Agents in
Healthcare
AI Agents in healthcare ii
Table of Contents
Executive summary 4
The AI revolution in healthcare: why now? 4
ROI snapshot: cost savings and efficiency gains 5
1
What makes AI assistants in healthcare tick? 7
Challenges in implementing Gen AI in clinical settings 8
Specialty-specific implementation hurdles 8
3
Specialty-specific AI assistants 10
AI agents for cardiology 11
AI agents for oncology and hematology 12
AI agents for gastroenterology 13
AI agents for neurology and neurosurgery 14
AI agents for internal medicine 15
AI agents for endocrinology 16
AI agents for dermatology 17
AI agents for emergency medicine 18
AI agents for critical care physicians 19
AI agents for pathologists 20
AI agents for physical medicine and rehabilitation 21
4
A decision framework for Gen AI implementations 22
Assessment matrix 23
Build/Buy: Key evaluation criteria for vendors 23
Vendor selection checklist 23
5
Gen AI playbook for healthcare providers 24
Phase 1: Initial assessment and planning (weeks 1-2) 25
Current state analysis 25
Use case prioritization 25
Technical planning 25
6
The business case for AI assistants
in clinical settings
6
2
Table of Contents
Executive summary 4
The AI revolution in healthcare: why now? 4
ROI snapshot: cost savings and efficiency gains 5
1
What makes AI assistants in healthcare tick? 7
Challenges in implementing Gen AI in clinical settings 8
Specialty-specific implementation hurdles 8
3
Specialty-specific AI assistants 10
AI agents for cardiology 11
AI agents for oncology and hematology 12
AI agents for gastroenterology 13
AI agents for neurology and neurosurgery 14
AI agents for internal medicine 15
AI agents for endocrinology 16
AI agents for dermatology 17
AI agents for emergency medicine 18
AI agents for critical care physicians 19
AI agents for pathologists 20
AI agents for physical medicine and rehabilitation 21
4
A decision framework for Gen AI implementations 22
Assessment matrix 23
Build/Buy: Key evaluation criteria for vendors 23
Vendor selection checklist 23
5
Gen AI playbook for healthcare providers 24
Phase 1: Initial assessment and planning (weeks 1-2) 25
Current state analysis 25
Use case prioritization 25
Technical planning 25
6
The business case for AI assistants
in clinical settings
6
2
AI Agents in healthcare iii
Phase 2: Pilot program design (weeks 3-6)
Phase 3: Full-scale implementation (weeks 7-8)
How we support your AI journey
Success metrics framework
Next steps Next steps
Funding support for Gen AI
34
34
Solution development
Organization-wide rollout
Discovery workshop process
Clinical outcomes
Training and change management
Performance monitoring
Proof of concept development
Operational efficiency
Controlled deployment
Continuous optimization
Partnership and support model
Financial impact
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Ready to transform your healthcare practice?
Featured case study:
Case study 2:
Case study 3:
30
31
32
33
Max healthcare—longitudinal patient data revolution
Atria Healthcare—intelligent patient profiling
Next-gen retinal imaging innovation
7
Phase 2: Pilot program design (weeks 3-6)
Phase 3: Full-scale implementation (weeks 7-8)
How we support your AI journey
Success metrics framework
Next steps Next steps
Funding support for Gen AI
34
34
Solution development
Organization-wide rollout
Discovery workshop process
Clinical outcomes
Training and change management
Performance monitoring
Proof of concept development
Operational efficiency
Controlled deployment
Continuous optimization
Partnership and support model
Financial impact
26
27
28
29
26
27
28
29
26
27
28
29
26
27
28
29
Ready to transform your healthcare practice?
Featured case study:
Case study 2:
Case study 3:
30
31
32
33
Max healthcare—longitudinal patient data revolution
Atria Healthcare—intelligent patient profiling
Next-gen retinal imaging innovation
7
AI Agents in healthcare 4
1
Executive summary
The AI revolution in healthcare:
why now?
Healthcare is at a tipping point. With deep domain expertise in
healthcare, life sciences, and financial services, we help clients
build generative AI pilots in just 8 weeks, leveraging enterprise-
grade LLM boilerplates. The convergence of advanced AI, cloud
computing, and the explosion of structured and unstructured
healthcare data has created unprecedented opportunities to
reimagine patient care delivery, clinician workflows, and operational
efficiency.
This eBook is your practical guide to navigating this transformation.
It not only outlines why AI adoption is urgent but also how to go
about it, step by step. Inside, you'll find:
? Real-world business cases and ROI analysis for hospital
administrators and clinical decision-makers?
? Key use cases and solution architectures across departments,
clinical documentation, diagnostics, patient triage, and
operations?
? Implementation frameworks and best practices for piloting and
scaling GenAI within your healthcare organizatio?
? Case studies from smart hospitals already realizing significant
improvements in cost, quality, and care coordinatio?
? Technical deep dives into how AI assistants work, including
multimodal input handling, NLP, integration with EHRs, and real-
time learning
1
Executive summary
The AI revolution in healthcare:
why now?
Healthcare is at a tipping point. With deep domain expertise in
healthcare, life sciences, and financial services, we help clients
build generative AI pilots in just 8 weeks, leveraging enterprise-
grade LLM boilerplates. The convergence of advanced AI, cloud
computing, and the explosion of structured and unstructured
healthcare data has created unprecedented opportunities to
reimagine patient care delivery, clinician workflows, and operational
efficiency.
This eBook is your practical guide to navigating this transformation.
It not only outlines why AI adoption is urgent but also how to go
about it, step by step. Inside, you'll find:
? Real-world business cases and ROI analysis for hospital
administrators and clinical decision-makers?
? Key use cases and solution architectures across departments,
clinical documentation, diagnostics, patient triage, and
operations?
? Implementation frameworks and best practices for piloting and
scaling GenAI within your healthcare organizatio?
? Case studies from smart hospitals already realizing significant
improvements in cost, quality, and care coordinatio?
? Technical deep dives into how AI assistants work, including
multimodal input handling, NLP, integration with EHRs, and real-
time learning
AI Agents in healthcare 5
Whether you’re a CMO, CIO, or department head, this eBook will
help you evaluate the impact, prioritize use cases, and build a
compelling business case for AI adoption within your organization.
Financial impact:
Z Average annual savings of $2.3M per 300-bed hospitalX
Z Reduced malpractice insurance premiums through
improved diagnostic accuracyX
Z Decreased length of stay through optimized treatment
protocolsX
Z Enhanced revenue capture through improved
documentation and coding
Clinical impact:
Z Earlier disease detection and interventiot
Z Personalized treatment recommendations based on
comprehensive data analysisX
Z Reduced medication errors and adverse drug eventsX
Z Improved care coordination across departments and
specialties
Operational impact:
Z Streamlined workflows and reduced redundant processesX
Z Optimized resource allocation and staff schedulingX
Z Enhanced capacity management and patient flo?
Z Improved supply chain management and inventory
control
ROI snapshot: cost savings and
efficiency gains
Whether you’re a CMO, CIO, or department head, this eBook will
help you evaluate the impact, prioritize use cases, and build a
compelling business case for AI adoption within your organization.
Financial impact:
Z Average annual savings of $2.3M per 300-bed hospitalX
Z Reduced malpractice insurance premiums through
improved diagnostic accuracyX
Z Decreased length of stay through optimized treatment
protocolsX
Z Enhanced revenue capture through improved
documentation and coding
Clinical impact:
Z Earlier disease detection and interventiot
Z Personalized treatment recommendations based on
comprehensive data analysisX
Z Reduced medication errors and adverse drug eventsX
Z Improved care coordination across departments and
specialties
Operational impact:
Z Streamlined workflows and reduced redundant processesX
Z Optimized resource allocation and staff schedulingX
Z Enhanced capacity management and patient flo?
Z Improved supply chain management and inventory
control
ROI snapshot: cost savings and
efficiency gains
AI Agents in healthcare 6
2
The business case for AI
assistants in clinical
settings
Some of the main challenges in healthcare have remained the same
over many years. The daily life of many doctors and the operational
nature of provider networks has not changed dramatically despite
many tech advancements.
These are the primary reasons why you should consider AI
assistants and copilots in your healthcare system.
AI assistants and copilots reduce documentation, support decision-
making, and free clinicians to focus on what matters most: patient
care.
It is our point of view that the time to wait and watch is over. There
is now ample evidence (from our own customer implementations
and other documented stories) that gen AI is not a fad and is here
to stay. We believe that adopting AI now will give advantages that
compound over time. Late adopters will struggle to catch up.
? The burnout crisis : In a recent AMIA survey, 74% of physicians
said the time spent on documentation impeded patient care:
Administrative burden is crushing clinical excellence?
? Rising costs, shrinking margins : Healthcare costs continue
climbing while reimbursements decline.?
? Quality under pressure : With increasing patient volumes and
staff shortages, maintaining consistent care quality becomes
increasingly challenging without increasing capacity.
2
The business case for AI
assistants in clinical
settings
Some of the main challenges in healthcare have remained the same
over many years. The daily life of many doctors and the operational
nature of provider networks has not changed dramatically despite
many tech advancements.
These are the primary reasons why you should consider AI
assistants and copilots in your healthcare system.
AI assistants and copilots reduce documentation, support decision-
making, and free clinicians to focus on what matters most: patient
care.
It is our point of view that the time to wait and watch is over. There
is now ample evidence (from our own customer implementations
and other documented stories) that gen AI is not a fad and is here
to stay. We believe that adopting AI now will give advantages that
compound over time. Late adopters will struggle to catch up.
? The burnout crisis : In a recent AMIA survey, 74% of physicians
said the time spent on documentation impeded patient care:
Administrative burden is crushing clinical excellence?
? Rising costs, shrinking margins : Healthcare costs continue
climbing while reimbursements decline.?
? Quality under pressure : With increasing patient volumes and
staff shortages, maintaining consistent care quality becomes
increasingly challenging without increasing capacity.
AI Agents in healthcare 7
3
What makes AI assistants
in healthcare tick?
Think of AI agents as your most reliable residents, available 24/7,
never tired, constantly learning, and backed by the latest medical
research. They process vast amounts of data instantly and provide
evidence-based recommendations:
j Multi-modal processing: Combines text, images, lab results,
and sensor data\
j Contextual understanding: Interprets medical terminology and
clinical context\
j Real-time learning: Continuously improves from new data and
outcomes\
j Seamless integration: Works within existing workflows without
disruption.
3
What makes AI assistants
in healthcare tick?
Think of AI agents as your most reliable residents, available 24/7,
never tired, constantly learning, and backed by the latest medical
research. They process vast amounts of data instantly and provide
evidence-based recommendations:
j Multi-modal processing: Combines text, images, lab results,
and sensor data\
j Contextual understanding: Interprets medical terminology and
clinical context\
j Real-time learning: Continuously improves from new data and
outcomes\
j Seamless integration: Works within existing workflows without
disruption.
AI Agents in healthcare 8
Gen AI assistants and agents integrate smoothly
with EHRs, PACS, laboratory systems, and
monitoring devices. In many cases, there is no rip-
and-replace required, just enhanced capabilities on
top of your existing systems. However, be aware of
potential challenges:
Challenges in
implementing Gen AI
in clinical settings
K Data integration complexities: EHR
fragmentation remains the biggest hurdle.
Success requires robust data standardization
and real-time synchronization capabilities.
K Staff training and adoption: Change
management is critical. The key is seamless
workflow integration that enhances rather than
disrupts existing processes. Clinical buy-in early
will offset a lot of frustration.
K Regulatory compliance requirements: HIPAA-
grade compliance, FDA pathways, and audit
trails are non-negotiable. Choose partners with
proven healthcare compliance expertise.
K Human-in-the-loop (HITL): Always ask about
HITL paths. In clinical settings, the doctor owns
the final decisions.
Specialty-specific
implementation hurdles
Clinical environments are complex. As many
technology providers have learnt over the years,
there is no one-size-fits-all. In our experience, here
are some unique challenges you may encounter
while implementing AI agents for different
specialties:
KCardiology challenges: ECG standardization
across devices, real-time processing demands
KEmergency medicine challenges:
KCritical care challenges:
KNeurology challenges:
Zero-
tolerance for delays, 24/7 reliability
requirements?
Life-critical decisions
with no room for error?
Complex neuroimaging
processing, surgical integration needs.
KInternal medicine and subspecialties
challenges: Managing complex multi-system
diseases, coordinating comprehensive care
across multiple conditions
KInternal medicine and subspecialties
challenges:
KEndocrinology challenges:
KDermatology challenges:
KEmergency medicine challenges:
Managing complex multi-system
diseases, coordinating comprehensive care
across multiple conditions?
Balancing delicate
hormonal systems, managing lifelong chronic
metabolic disorders requiring precise titration?
Distinguishing
between thousands o f similar-appearing
conditions, addressing both cosmetic and life-
threatening diseases?
Zero-
tolerance for delays, 24/7 reliability
requirements demanding immediate critical
decision-making under pressure
Gen AI assistants and agents integrate smoothly
with EHRs, PACS, laboratory systems, and
monitoring devices. In many cases, there is no rip-
and-replace required, just enhanced capabilities on
top of your existing systems. However, be aware of
potential challenges:
Challenges in
implementing Gen AI
in clinical settings
K Data integration complexities: EHR
fragmentation remains the biggest hurdle.
Success requires robust data standardization
and real-time synchronization capabilities.
K Staff training and adoption: Change
management is critical. The key is seamless
workflow integration that enhances rather than
disrupts existing processes. Clinical buy-in early
will offset a lot of frustration.
K Regulatory compliance requirements: HIPAA-
grade compliance, FDA pathways, and audit
trails are non-negotiable. Choose partners with
proven healthcare compliance expertise.
K Human-in-the-loop (HITL): Always ask about
HITL paths. In clinical settings, the doctor owns
the final decisions.
Specialty-specific
implementation hurdles
Clinical environments are complex. As many
technology providers have learnt over the years,
there is no one-size-fits-all. In our experience, here
are some unique challenges you may encounter
while implementing AI agents for different
specialties:
KCardiology challenges: ECG standardization
across devices, real-time processing demands
KEmergency medicine challenges:
KCritical care challenges:
KNeurology challenges:
Zero-
tolerance for delays, 24/7 reliability
requirements?
Life-critical decisions
with no room for error?
Complex neuroimaging
processing, surgical integration needs.
KInternal medicine and subspecialties
challenges: Managing complex multi-system
diseases, coordinating comprehensive care
across multiple conditions
KInternal medicine and subspecialties
challenges:
KEndocrinology challenges:
KDermatology challenges:
KEmergency medicine challenges:
Managing complex multi-system
diseases, coordinating comprehensive care
across multiple conditions?
Balancing delicate
hormonal systems, managing lifelong chronic
metabolic disorders requiring precise titration?
Distinguishing
between thousands o f similar-appearing
conditions, addressing both cosmetic and life-
threatening diseases?
Zero-
tolerance for delays, 24/7 reliability
requirements demanding immediate critical
decision-making under pressure
AI Agents in healthcare 9
; Critical care medicine challenges:
; Laboratory medicine and pathology challenges:
; Orthopedics challenges:
; Urology challenges:
; Psychiatry and psychology challenges:
; Pulmonology challenges:
; Nephrology challenges:
; Rheumatology challenges:
Managing
unstable patients requiring constant monitoring,
making life-or-death decisions with incomplete
information`
Ensuring absolute accuracy in diagnostic
testing, interpreting complex results affecting
patient outcomes`
Combining surgical
precision with biomechanical expertise,
managing both acute trauma and degenerative
conditions`
Addressing sensitive
intimate health issues, performing delicate
procedures in anatomically challenging locations`
Treating invisible illnesses with subjective
symptoms, managing patient safety and societal
stigmZ
Managing life-
threatening respiratory emergencies, treating
progressive diseases with limited reversible
treatment options`
Handling irreversible
kidney damage, managing complex dialysis
schedules and transplant coordination
requirements`
Diagnosing elusive
autoimmune conditions, balancing
immunosuppression benefits against infection
risks and complications
; Radiation oncology challenges:
; Anesthesiology challenges:
; Otolaryngology (ENT) challenges:
; Ophthalmology challenges:
; Radiology challenges:
; Pathology challenges:
; Physical medicine and rehabilitation
challenges:
Delivering
precise cancer treatment, balancing tumor
destruction with healthy tissue preservation
strategies`
Ensuring patient
safety during unconsciousness, managing
unpredictable reactions and maintaining
physiological stability`
Operating in
confined anatomical spaces, managing`
Performing
microscopic surgery on irreplaceable sensory
organs, preventing permanent vision loss
complications`
Interpreting subtle
imaging findings accurately, managing high-
volume studies while maintaining diagnostic
precision standards`
Providing definitive
diagnoses from tissue samples, bearing
responsibility for cancer staging and treatment
decision?
Restoring function after devastating
injuries, managing complex disabilities requiring
long-term coordination.
; Critical care medicine challenges:
; Laboratory medicine and pathology challenges:
; Orthopedics challenges:
; Urology challenges:
; Psychiatry and psychology challenges:
; Pulmonology challenges:
; Nephrology challenges:
; Rheumatology challenges:
Managing
unstable patients requiring constant monitoring,
making life-or-death decisions with incomplete
information`
Ensuring absolute accuracy in diagnostic
testing, interpreting complex results affecting
patient outcomes`
Combining surgical
precision with biomechanical expertise,
managing both acute trauma and degenerative
conditions`
Addressing sensitive
intimate health issues, performing delicate
procedures in anatomically challenging locations`
Treating invisible illnesses with subjective
symptoms, managing patient safety and societal
stigmZ
Managing life-
threatening respiratory emergencies, treating
progressive diseases with limited reversible
treatment options`
Handling irreversible
kidney damage, managing complex dialysis
schedules and transplant coordination
requirements`
Diagnosing elusive
autoimmune conditions, balancing
immunosuppression benefits against infection
risks and complications
; Radiation oncology challenges:
; Anesthesiology challenges:
; Otolaryngology (ENT) challenges:
; Ophthalmology challenges:
; Radiology challenges:
; Pathology challenges:
; Physical medicine and rehabilitation
challenges:
Delivering
precise cancer treatment, balancing tumor
destruction with healthy tissue preservation
strategies`
Ensuring patient
safety during unconsciousness, managing
unpredictable reactions and maintaining
physiological stability`
Operating in
confined anatomical spaces, managing`
Performing
microscopic surgery on irreplaceable sensory
organs, preventing permanent vision loss
complications`
Interpreting subtle
imaging findings accurately, managing high-
volume studies while maintaining diagnostic
precision standards`
Providing definitive
diagnoses from tissue samples, bearing
responsibility for cancer staging and treatment
decision?
Restoring function after devastating
injuries, managing complex disabilities requiring
long-term coordination.
AI Agents in healthcare 10
4
Specialty-specific
AI assistants
Consider this chapter as a handy ‘art of the possible’ for
discussions with your executives, boards, CMOs, doctors, and
technologists. We have put together this list of AI assistants and
agents based on our own implementations, conversations with
healthcare professionals, and an assessment of what is possible
with the gen AI technology we already have.
There is a bigger list of AI assistants and copilots (more than 150)
in our internal research and we will be pleased to share it with you.
Ask us and we will email it. For now, we have focused on 50 use
cases that we believe are great to pilot and prove RoI.
4
Specialty-specific
AI assistants
Consider this chapter as a handy ‘art of the possible’ for
discussions with your executives, boards, CMOs, doctors, and
technologists. We have put together this list of AI assistants and
agents based on our own implementations, conversations with
healthcare professionals, and an assessment of what is possible
with the gen AI technology we already have.
There is a bigger list of AI assistants and copilots (more than 150)
in our internal research and we will be pleased to share it with you.
Ask us and we will email it. For now, we have focused on 50 use
cases that we believe are great to pilot and prove RoI.
AI Agents in healthcare 11
AI agents for cardiology
This AI agent analyzes ECGs to detect critical
conditions like arrhythmias, STEMI, AFIB, heart
blocks, and ischemic changes. It flags patterns
instantly with high accuracy and speed.
Standardized reports ensure faster, safer clinical
decisions without missing key cardiac events.
Eliminates the burden of manual ECG evaluation,
reduces diagnostic errors from fatigue or
distraction, and ensures no life-threatening
arrhythmias are missed during critical care periods.
This agent tracks symptoms, vitals, and behavior to
predict heart failure decompensation. It builds a
personalized baseline and alerts doctors about
potential acute events. Enables proactive care
adjustments and reduces emergency interventions.
Transforms reactive emergency care into proactive
management, reduces urgent after-hours calls from
deteriorating patients, and provides data-driven
insights for optimizing heart failure medications and
care plans.
This copilot reviews imaging to suggest catheter
paths, wire choices, and stent sizing. It simulates
procedures, predicts complications, and optimizes
contrast use. The copilot helps cardiologists reduce
planning time and improve procedural precision.
Reduces procedural planning time, minimizes trial-
and-error during complex interventions, and
provides confidence in approach selection, leading
to shorter procedure times and improved patient
safety outcomes.
Automated ECG analysis agent Heart failure monitoring agent
Coronary angiography planning
copilot
This copilot reviews imaging to suggest catheter
paths, wire choices, and stent sizing. It simulates
procedures, predicts complications, and optimizes
contrast use. The copilot helps cardiologists reduce
planning time and improve procedural precision.
Reduces procedural planning time, minimizes trial-
and-error during complex interventions, and
provides confidence in approach selection, leading
to shorter procedure times and improved patient
safety outcomes.
This assistant auto-analyzes 2D/3D echo images to
extract key cardiac measurements. It generates
standardized reports, highlights anomalies, and
ensures consistency in reports across cases. This
can accelerate echo interpretation from 30–45
minutes to just minutes.
Dramatically reduces time spent on routine
measurements, eliminates inter-observer variability
in readings, and allows doctors to focus on clinical
interpretation rather than technical analysis tasks.
Cardiac risk stratification
AI copilot
Echocardiogram interpretation
AI assistant
AI agents for cardiology
This AI agent analyzes ECGs to detect critical
conditions like arrhythmias, STEMI, AFIB, heart
blocks, and ischemic changes. It flags patterns
instantly with high accuracy and speed.
Standardized reports ensure faster, safer clinical
decisions without missing key cardiac events.
Eliminates the burden of manual ECG evaluation,
reduces diagnostic errors from fatigue or
distraction, and ensures no life-threatening
arrhythmias are missed during critical care periods.
This agent tracks symptoms, vitals, and behavior to
predict heart failure decompensation. It builds a
personalized baseline and alerts doctors about
potential acute events. Enables proactive care
adjustments and reduces emergency interventions.
Transforms reactive emergency care into proactive
management, reduces urgent after-hours calls from
deteriorating patients, and provides data-driven
insights for optimizing heart failure medications and
care plans.
This copilot reviews imaging to suggest catheter
paths, wire choices, and stent sizing. It simulates
procedures, predicts complications, and optimizes
contrast use. The copilot helps cardiologists reduce
planning time and improve procedural precision.
Reduces procedural planning time, minimizes trial-
and-error during complex interventions, and
provides confidence in approach selection, leading
to shorter procedure times and improved patient
safety outcomes.
Automated ECG analysis agent Heart failure monitoring agent
Coronary angiography planning
copilot
This copilot reviews imaging to suggest catheter
paths, wire choices, and stent sizing. It simulates
procedures, predicts complications, and optimizes
contrast use. The copilot helps cardiologists reduce
planning time and improve procedural precision.
Reduces procedural planning time, minimizes trial-
and-error during complex interventions, and
provides confidence in approach selection, leading
to shorter procedure times and improved patient
safety outcomes.
This assistant auto-analyzes 2D/3D echo images to
extract key cardiac measurements. It generates
standardized reports, highlights anomalies, and
ensures consistency in reports across cases. This
can accelerate echo interpretation from 30–45
minutes to just minutes.
Dramatically reduces time spent on routine
measurements, eliminates inter-observer variability
in readings, and allows doctors to focus on clinical
interpretation rather than technical analysis tasks.
Cardiac risk stratification
AI copilot
Echocardiogram interpretation
AI assistant
AI Agents in healthcare 12
AI agents for oncology and hematology
This agent analyzes imaging (CT/MRI), pathology
reports, and biomarker profiles to determine
accurate TNM staging. It aligns with current
oncology guidelines and incorporates molecular
markers to enhance prognosis accuracy. The
system produces standardized reports with
confidence intervals, ensuring staging uniformity
across providers.
Ensures staging consistency, saves time on manual
assessments, and provides reliable survival data for
informed decisions and trial selection.
This agent tracks changes in tumor markers,
imaging, labs, and clinical symptoms across
treatment cycles. It detects early signs of response
or resistance before they manifest clinically,
D22@#E>?v;.@D(<E"AvE><A."A><E@>%v\
Enables timely intervention, reduces treatment
delays, and improves response tracking in complex
oncology cases.
The copilot analyzes HLA typing, minor antigens,
and clinical compatibility factors to rank optimal
donor-recipient pairs. It assesses risks like graft-
versus-host disease and calculates transplant
success probabilities. Patient-specific and center-
level outcomes data enhance match
.A(@99A>-D<E@>0%\
Improves match accuracy, minimizes GVHD risk,
and expands the usable donor pool, enhancing
transplant success.
This AI advisor integrates tumor genomics,
pharmacogenomics, and clinical guidelines to
suggest personalized therapies. It highlights
actionable mutations, predicts drug response/
resistance, and prioritizes treatments based on
molecular profiling. The engine updates
continuously as new evidence and trial data
A9A.?A%\
Boosts use of precision therapies, avoids
ineffective treatments, and enhances outcomes via
genomics-guided care.
Cancer staging and prognosis
agent
Treatment response monitoring
agent
Bone marrow transplant matching
copilot
Precision oncology advisor
The copilot dynamically recommends
chemotherapy dosing by monitoring
pharmacokinetics, organ function, blood counts,
and toxicity patterns. It predicts adverse reactions
before onset and recommends safe dose
modifications based on real-time patient data. The
system continuously learns from patient-specific
trends to fine-tune regimens.
Reduces toxicity-related hospitalizations, supports
confident dosing, and improves treatment
adherence and patient safety.
Chemotherapy dosing copilot
Blood smear analysis assistant
Using AI -driven microscopy, this assistant
analyzes digitized smears to identify abnormal
morphology, blasts, dysplasia, or parasites. It
delivers rapid differential counts and highlights
urgent abnormalities with annotated visuals. Quality
control metrics ensure consistent, high-confidence
interpretations.
Delivers faster diagnostics, reduces interpretive
variability, and enables timely treatment for critical
hematologic cases.
AI agents for oncology and hematology
This agent analyzes imaging (CT/MRI), pathology
reports, and biomarker profiles to determine
accurate TNM staging. It aligns with current
oncology guidelines and incorporates molecular
markers to enhance prognosis accuracy. The
system produces standardized reports with
confidence intervals, ensuring staging uniformity
across providers.
Ensures staging consistency, saves time on manual
assessments, and provides reliable survival data for
informed decisions and trial selection.
This agent tracks changes in tumor markers,
imaging, labs, and clinical symptoms across
treatment cycles. It detects early signs of response
or resistance before they manifest clinically,
D22@#E>?v;.@D(<E"AvE><A."A><E@>%v\
Enables timely intervention, reduces treatment
delays, and improves response tracking in complex
oncology cases.
The copilot analyzes HLA typing, minor antigens,
and clinical compatibility factors to rank optimal
donor-recipient pairs. It assesses risks like graft-
versus-host disease and calculates transplant
success probabilities. Patient-specific and center-
level outcomes data enhance match
.A(@99A>-D<E@>0%\
Improves match accuracy, minimizes GVHD risk,
and expands the usable donor pool, enhancing
transplant success.
This AI advisor integrates tumor genomics,
pharmacogenomics, and clinical guidelines to
suggest personalized therapies. It highlights
actionable mutations, predicts drug response/
resistance, and prioritizes treatments based on
molecular profiling. The engine updates
continuously as new evidence and trial data
A9A.?A%\
Boosts use of precision therapies, avoids
ineffective treatments, and enhances outcomes via
genomics-guided care.
Cancer staging and prognosis
agent
Treatment response monitoring
agent
Bone marrow transplant matching
copilot
Precision oncology advisor
The copilot dynamically recommends
chemotherapy dosing by monitoring
pharmacokinetics, organ function, blood counts,
and toxicity patterns. It predicts adverse reactions
before onset and recommends safe dose
modifications based on real-time patient data. The
system continuously learns from patient-specific
trends to fine-tune regimens.
Reduces toxicity-related hospitalizations, supports
confident dosing, and improves treatment
adherence and patient safety.
Chemotherapy dosing copilot
Blood smear analysis assistant
Using AI -driven microscopy, this assistant
analyzes digitized smears to identify abnormal
morphology, blasts, dysplasia, or parasites. It
delivers rapid differential counts and highlights
urgent abnormalities with annotated visuals. Quality
control metrics ensure consistent, high-confidence
interpretations.
Delivers faster diagnostics, reduces interpretive
variability, and enables timely treatment for critical
hematologic cases.
AI Agents in healthcare 13
This AI agent analyzes real-time colonoscopy
footage using computer vision to automatically
identify and classify polyps during procedures. It
distinguishes between adenomatous and
hyperplastic polyps while alerting physicians to
subtle lesions.
Significantly increases adenoma detection rates,
reduces interval cancer risk, and standardizes
polyp identification across skill levels for improved
screening outcomes.
This AI copilot continuously analyzes inflammatory
bowel disease biomarkers, imaging data, and
patient symptoms to assess disease activity. It
tracks inflammatory markers and predicts flare-ups
while providing personalized treatment
PiceePTtadkcT/rK
Optimizes treatment timing through predictive
analytics, prevents disease flare-ups via early
intervention, and improves long-term outcomes
through personalized monitoring protocols.
Colonoscopy polyp detection
agent
IBD activity monitoring copilot
AI agents for gastroenterology
This AI assistant analyzes non-invasive imaging,
including elastography and MRI to accurately stage
liver fibrosis without tissue samples. It combines
multiple assessment tools and provides METAVIR-
equivalent staging with cirrhosis risk stratification.
Reduces dependency on invasive liver biopsies,
enables early intervention strategies, and facilitates
continuous monitoring of fibrosis progression in
chronic liver disease patients. Liver fibrosis assessment assistant
This AI agent performs automated analysis of
endoscopic images to detect precancerous lesions
and early malignancies throughout the digestive
tract. It identifies dysplastic changes and provides
real-time alerts with detailed morphological
analysis during procedures.
Improves early cancer detection rates through
enhanced sensitivity, standardizes screening
protocols across providers, and reduces diagnostic
variability in endoscopic interpretation.
Endoscopic image analysis agent
This AI optimizer creates personalized dietary
recommendations by analyzing digestive
conditions, symptom patterns, and food
intolerances. It considers inflammatory markers
and microbiome composition to design optimal
meal plans and adjust recommendations based on
treatment response.
Improves symptom management through
evidence-based nutrition plans, enhances
treatment compliance via personalized
approaches, and optimizes therapeutic outcomes
in functional digestive disorders.
Nutritional therapy optimizer
This AI predictor integrates multiple risk factors
including family history, genetic markers, and
imaging findings to identify high-risk pancreatic
cancer patients. It analyzes trends and calculates
personalized risk scores for appropriate screening
PiceePTtadkcT/rK
Enables early detection through risk-stratified
screening, improves screening protocol
effectiveness via personalized approaches, and
identifies candidates for intensive surveillance
programs.
Pancreatic cancer risk predictor
This AI agent analyzes real-time colonoscopy
footage using computer vision to automatically
identify and classify polyps during procedures. It
distinguishes between adenomatous and
hyperplastic polyps while alerting physicians to
subtle lesions.
Significantly increases adenoma detection rates,
reduces interval cancer risk, and standardizes
polyp identification across skill levels for improved
screening outcomes.
This AI copilot continuously analyzes inflammatory
bowel disease biomarkers, imaging data, and
patient symptoms to assess disease activity. It
tracks inflammatory markers and predicts flare-ups
while providing personalized treatment
PiceePTtadkcT/rK
Optimizes treatment timing through predictive
analytics, prevents disease flare-ups via early
intervention, and improves long-term outcomes
through personalized monitoring protocols.
Colonoscopy polyp detection
agent
IBD activity monitoring copilot
AI agents for gastroenterology
This AI assistant analyzes non-invasive imaging,
including elastography and MRI to accurately stage
liver fibrosis without tissue samples. It combines
multiple assessment tools and provides METAVIR-
equivalent staging with cirrhosis risk stratification.
Reduces dependency on invasive liver biopsies,
enables early intervention strategies, and facilitates
continuous monitoring of fibrosis progression in
chronic liver disease patients. Liver fibrosis assessment assistant
This AI agent performs automated analysis of
endoscopic images to detect precancerous lesions
and early malignancies throughout the digestive
tract. It identifies dysplastic changes and provides
real-time alerts with detailed morphological
analysis during procedures.
Improves early cancer detection rates through
enhanced sensitivity, standardizes screening
protocols across providers, and reduces diagnostic
variability in endoscopic interpretation.
Endoscopic image analysis agent
This AI optimizer creates personalized dietary
recommendations by analyzing digestive
conditions, symptom patterns, and food
intolerances. It considers inflammatory markers
and microbiome composition to design optimal
meal plans and adjust recommendations based on
treatment response.
Improves symptom management through
evidence-based nutrition plans, enhances
treatment compliance via personalized
approaches, and optimizes therapeutic outcomes
in functional digestive disorders.
Nutritional therapy optimizer
This AI predictor integrates multiple risk factors
including family history, genetic markers, and
imaging findings to identify high-risk pancreatic
cancer patients. It analyzes trends and calculates
personalized risk scores for appropriate screening
PiceePTtadkcT/rK
Enables early detection through risk-stratified
screening, improves screening protocol
effectiveness via personalized approaches, and
identifies candidates for intensive surveillance
programs.
Pancreatic cancer risk predictor
AI Agents in healthcare 14
AI agents for neurology and neurosurgery
This AI agent performs rapid analysis of brain
imaging including CT and MRI to identify acute
stroke within minutes of acquisition. It
differentiates ischemic from hemorrhagic strokes
and calculates severity scores for immediate triage
prioritization.
Reduces door-to-needle time through accelerated
diagnosis, improves functional recovery outcomes
via early intervention, and optimizes emergency
stroke care protocols.
Stroke detection and triage agent
This AI copilot continuously monitors EEG patterns
and physiological signals to predict seizure
onset before clinical manifestation. It analyzes
brainwave anomalies and patient-specific triggers
to provide early warning alerts for preventive
interventions.
Reduces seizure frequency through predictive
intervention, improves quality of life via proactive
management, and enables personalized epilepsy
treatment strategies.
Epilepsy seizure prediction copilot
This AI assistant automatically detects brain
abnormalities including tumors, lesions, and
structural changes from MRI and CT scans. It
provides detailed annotations and differential
diagnoses while flagging urgent findings for
immediate attention.
Accelerates diagnosis through automated
screening, reduces radiologist workload
significantly, and improves detection accuracy for
subtle neurological abnormalities.
Neuroimaging analysis assistant
This AI agent can continuously monitor tremor
patterns, gait abnormalities, and movement
characteristics when wearable sensors and video
are available. It quantifies symptom severity and
tracks medication response in real-time for
Parkinson 's and related disorders.
Optimizes medication timing through objective
monitoring, tracks disease progression accurately,
and enables data-driven treatment adjustments for
movement disorders. Movement disorder assessment
agent
This AI copilot creates detailed 3D brain mapping
and surgical navigation plans using advanced
imaging and anatomical modeling. It identifies
critical structures, predicts surgical risks, and
optimizes approach routes for maximum safety and
efficacy.
Reduces surgical complications through enhanced
planning, preserves critical neurological function
via precise navigation, and improves surgical
outcomes through risk stratification.
This AI agent performs longitudinal analysis of
cognitive function using neuropsychological tests,
biomarkers, observations, and imaging data to
track dementia progression. It integrates multiple
assessment modalities to predict decline
trajectories and treatment responses.
Enables early intervention through predictive
analytics, optimizes personalized care planning,
and improves dementia management through
comprehensive monitoring protocols.
Cognitive decline monitoring agent
Surgical planning copilot
AI agents for neurology and neurosurgery
This AI agent performs rapid analysis of brain
imaging including CT and MRI to identify acute
stroke within minutes of acquisition. It
differentiates ischemic from hemorrhagic strokes
and calculates severity scores for immediate triage
prioritization.
Reduces door-to-needle time through accelerated
diagnosis, improves functional recovery outcomes
via early intervention, and optimizes emergency
stroke care protocols.
Stroke detection and triage agent
This AI copilot continuously monitors EEG patterns
and physiological signals to predict seizure
onset before clinical manifestation. It analyzes
brainwave anomalies and patient-specific triggers
to provide early warning alerts for preventive
interventions.
Reduces seizure frequency through predictive
intervention, improves quality of life via proactive
management, and enables personalized epilepsy
treatment strategies.
Epilepsy seizure prediction copilot
This AI assistant automatically detects brain
abnormalities including tumors, lesions, and
structural changes from MRI and CT scans. It
provides detailed annotations and differential
diagnoses while flagging urgent findings for
immediate attention.
Accelerates diagnosis through automated
screening, reduces radiologist workload
significantly, and improves detection accuracy for
subtle neurological abnormalities.
Neuroimaging analysis assistant
This AI agent can continuously monitor tremor
patterns, gait abnormalities, and movement
characteristics when wearable sensors and video
are available. It quantifies symptom severity and
tracks medication response in real-time for
Parkinson 's and related disorders.
Optimizes medication timing through objective
monitoring, tracks disease progression accurately,
and enables data-driven treatment adjustments for
movement disorders. Movement disorder assessment
agent
This AI copilot creates detailed 3D brain mapping
and surgical navigation plans using advanced
imaging and anatomical modeling. It identifies
critical structures, predicts surgical risks, and
optimizes approach routes for maximum safety and
efficacy.
Reduces surgical complications through enhanced
planning, preserves critical neurological function
via precise navigation, and improves surgical
outcomes through risk stratification.
This AI agent performs longitudinal analysis of
cognitive function using neuropsychological tests,
biomarkers, observations, and imaging data to
track dementia progression. It integrates multiple
assessment modalities to predict decline
trajectories and treatment responses.
Enables early intervention through predictive
analytics, optimizes personalized care planning,
and improves dementia management through
comprehensive monitoring protocols.
Cognitive decline monitoring agent
Surgical planning copilot
AI Agents in healthcare 15
AI agents for internal medicine
This AI agent processes comprehensive patient
history, laboratory results, and imaging data to
create detailed treatment roadmaps. It identifies
key medical patterns, comorbidities, and risk
factors while generating prioritized clinical
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Fast-tracks medical history interpretation through
automated analysis, improves diagnostic accuracy
via comprehensive profiling, and streamlines
clinical decision-making for complex patients.
Automated patient profiler
This AI agent provides real-time analysis of patient
data to suggest differential diagnoses and
evidence-based treatment options. It integrates
clinical guidelines, patient-specific factors, and
recent medical literature for comprehensive
decision support.
Reduces diagnostic errors through systematic
analysis, standardizes care quality across
providers, and enhances clinical reasoning with
evidence-based recommendations.
This AI copilot automatically reviews patient
medication lists to identify dangerous drug
interactions, contraindications, and dosing errors.
It cross-references patient allergies, kidney
function, and concurrent medications for safety
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Prevents adverse drug events through
comprehensive screening, reduces medication
errors significantly, and ensures safe
polypharmacy management in complex patients.
Clinical decision support agent
Medication reconciliation copilot
This AI assistant continuously monitors chronic
disease indicators and automatically adjusts
personalized care plans based on patient progress.
It tracks guideline adherence and recommends
timely interventions for optimal disease
control.
Improves guideline adherence through automated
monitoring, reduces disease-related complications
via proactive management, and optimizes chronic
care delivery for better outcomes. Chronic disease management
assistant
This AI agent integrates multiple predictive models
to calculate personalized risk scores for various
health conditions. It analyzes patient
demographics, biomarkers, and clinical history to
stratify risk and recommend appropriate
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Optimizes preventive care strategies through risk
stratification, reduces unnecessary testing via
targeted screening, and enables personalized
prevention protocols.
Risk assessment agent
This AI copilot automates discharge planning by
coordinating follow-up appointments, medication
reconciliation, and care instructions. It ensures
seamless transitions between care settings and
providers while optimizing post-discharge
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Reduces readmissions through systematic
transition planning, ensures continuity of care
across settings, and improves patient safety during
care transitions.
Care transition coordinator
AI agents for internal medicine
This AI agent processes comprehensive patient
history, laboratory results, and imaging data to
create detailed treatment roadmaps. It identifies
key medical patterns, comorbidities, and risk
factors while generating prioritized clinical
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Fast-tracks medical history interpretation through
automated analysis, improves diagnostic accuracy
via comprehensive profiling, and streamlines
clinical decision-making for complex patients.
Automated patient profiler
This AI agent provides real-time analysis of patient
data to suggest differential diagnoses and
evidence-based treatment options. It integrates
clinical guidelines, patient-specific factors, and
recent medical literature for comprehensive
decision support.
Reduces diagnostic errors through systematic
analysis, standardizes care quality across
providers, and enhances clinical reasoning with
evidence-based recommendations.
This AI copilot automatically reviews patient
medication lists to identify dangerous drug
interactions, contraindications, and dosing errors.
It cross-references patient allergies, kidney
function, and concurrent medications for safety
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Prevents adverse drug events through
comprehensive screening, reduces medication
errors significantly, and ensures safe
polypharmacy management in complex patients.
Clinical decision support agent
Medication reconciliation copilot
This AI assistant continuously monitors chronic
disease indicators and automatically adjusts
personalized care plans based on patient progress.
It tracks guideline adherence and recommends
timely interventions for optimal disease
control.
Improves guideline adherence through automated
monitoring, reduces disease-related complications
via proactive management, and optimizes chronic
care delivery for better outcomes. Chronic disease management
assistant
This AI agent integrates multiple predictive models
to calculate personalized risk scores for various
health conditions. It analyzes patient
demographics, biomarkers, and clinical history to
stratify risk and recommend appropriate
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Optimizes preventive care strategies through risk
stratification, reduces unnecessary testing via
targeted screening, and enables personalized
prevention protocols.
Risk assessment agent
This AI copilot automates discharge planning by
coordinating follow-up appointments, medication
reconciliation, and care instructions. It ensures
seamless transitions between care settings and
providers while optimizing post-discharge
460<+6'<02$g
Reduces readmissions through systematic
transition planning, ensures continuity of care
across settings, and improves patient safety during
care transitions.
Care transition coordinator
AI Agents in healthcare 16
AI agents for endocrinology
This AI copilot analyzes continuous glucose
monitoring data, meal intake, and activity patterns
to provide real-time personalized insulin dosing
recommendations. It learns individual response
patterns and adjusts recommendations based on
lifestyle factors.
Improves glycemic control through personalized
dosing algorithms, reduces hypoglycemic events
via predictive monitoring, and optimizes diabetes
management for better patient outcomes.
This AI agent automatically analyzes thyroid
ultrasound images to assess nodule characteristics
and malignancy risk. It applies standardized
scoring systems and determines appropriate
biopsy recommendations based on established
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Reduces unnecessary biopsies through accurate
risk assessment, improves cancer detection rates,
and standardizes thyroid nodule evaluation across
providers.
Diabetes management copilot
Thyroid nodule risk stratification
agent
This AI assistant personalizes hormone therapy
recommendations by analyzing patient symptoms,
laboratory values, and individual risk factors. It
adjusts dosing and formulations based on
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Improves symptom relief through personalized
therapy optimization, minimizes treatment side
effects via individualized dosing, and enhances
hormone replacement therapy outcomes.
Hormone replacement optimization
assistant
This AI agent performs automated analysis of
retinal photography to detect diabetic eye disease
and grade severity levels. It identifies hemorrhages,
exudates, and neovascularization while providing
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Increases screening compliance through accessible
automated analysis, prevents vision loss
complications via early detection, and improves
diabetic care coordination.
This AI agent integrates multiple metabolic
markers, including glucose levels, lipid profiles, and
blood pressure, to predict metabolic syndrome
development. It calculates personalized risk scores
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Enables early intervention through predictive risk
assessment, prevents progression to diabetes via
targeted prevention, and optimizes metabolic
health management strategies.
This AI copilot continuously monitors cortisol levels
and stress indicators to optimize hormone
replacement therapy dosing. It predicts adrenal
crisis risk and provides real-time dosing
adjustments for various stress situations.
Prevents life-threatening adrenal crises through
predictive monitoring, improves quality of life via
optimized hormone replacement, and enhances
adrenal insufficiency management.
Diabetic retinopathy screening
agent
Metabolic syndrome prediction
assistant
Adrenal insufficiency monitoring
copilot
AI agents for endocrinology
This AI copilot analyzes continuous glucose
monitoring data, meal intake, and activity patterns
to provide real-time personalized insulin dosing
recommendations. It learns individual response
patterns and adjusts recommendations based on
lifestyle factors.
Improves glycemic control through personalized
dosing algorithms, reduces hypoglycemic events
via predictive monitoring, and optimizes diabetes
management for better patient outcomes.
This AI agent automatically analyzes thyroid
ultrasound images to assess nodule characteristics
and malignancy risk. It applies standardized
scoring systems and determines appropriate
biopsy recommendations based on established
7*045=0.5?%S
Reduces unnecessary biopsies through accurate
risk assessment, improves cancer detection rates,
and standardizes thyroid nodule evaluation across
providers.
Diabetes management copilot
Thyroid nodule risk stratification
agent
This AI assistant personalizes hormone therapy
recommendations by analyzing patient symptoms,
laboratory values, and individual risk factors. It
adjusts dosing and formulations based on
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Improves symptom relief through personalized
therapy optimization, minimizes treatment side
effects via individualized dosing, and enhances
hormone replacement therapy outcomes.
Hormone replacement optimization
assistant
This AI agent performs automated analysis of
retinal photography to detect diabetic eye disease
and grade severity levels. It identifies hemorrhages,
exudates, and neovascularization while providing
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Increases screening compliance through accessible
automated analysis, prevents vision loss
complications via early detection, and improves
diabetic care coordination.
This AI agent integrates multiple metabolic
markers, including glucose levels, lipid profiles, and
blood pressure, to predict metabolic syndrome
development. It calculates personalized risk scores
1.4p(586::5.4?p3(5&5.20&5p0.25(&5.206.?%S
Enables early intervention through predictive risk
assessment, prevents progression to diabetes via
targeted prevention, and optimizes metabolic
health management strategies.
This AI copilot continuously monitors cortisol levels
and stress indicators to optimize hormone
replacement therapy dosing. It predicts adrenal
crisis risk and provides real-time dosing
adjustments for various stress situations.
Prevents life-threatening adrenal crises through
predictive monitoring, improves quality of life via
optimized hormone replacement, and enhances
adrenal insufficiency management.
Diabetic retinopathy screening
agent
Metabolic syndrome prediction
assistant
Adrenal insufficiency monitoring
copilot
AI Agents in healthcare 17
AI agents for dermatology
This AI agent performs automated dermoscopy
analysis to identify melanoma and suspicious skin
lesions. It analyzes color patterns, asymmetry, and
border characteristics while providing malignancy
risk scores.
Increases early cancer detection through enhanced
screening accuracy, reduces unnecessary biopsies
via improved risk stratification, and standardizes
skin cancer screening protocols.
This AI copilot provides standardized image
analysis to grade acne severity and track treatment
progress over time. It counts lesions, assesses
inflammation levels, and monitors therapeutic
response objectively.
Optimizes treatment selection through objective
severity assessment, monitors progress objectively
via standardized grading, and improves acne
management outcomes.
Skin cancer detection agent
Acne severity assessment copilot
Psoriasis activity monitoring
assistant
This AI assistant continuously assesses psoriasis
lesion severity using patient-submitted
It tracks disease activity, identifies flare-ups early,
and recommends treatment adjustments based
on progression patterns.
Improves treatment timing through continuous
monitoring, reduces disease flare-ups via early
intervention, and optimizes psoriasis management
through objective assessment.
Drug eruption identifier
This AI agent uses pattern recognition to identify
medication-induced skin reactions from clinical
photographs and patient history.
It correlates timing patterns and reaction
characteristics to identify causative medications.
Accelerates causative drug identification through
systematic analysis, prevents severe reactions via
early recognition, and improves adverse drug
reaction management.
This AI assistant automatically measures wound
dimensions and healing progress from smartphone
photographs. It tracks healing rates, identifies
complications early, and recommends appropriate
wound care protocol adjustments.
Optimizes wound care protocols through objective
monitoring, reduces healing time via early
intervention, and improves wound management
outcomes. Wound healing tracker
This AI copilot performs 3D facial analysis to
optimize aesthetic treatment planning and predict
outcomes. It simulates procedure results and
recommends optimal treatment approaches based
on individual facial anatomy.
Improves patient satisfaction through realistic
outcome prediction, optimizes cosmetic treatment
outcomes via precise planning, and enhances
aesthetic procedure success rates. Cosmetic procedure planner
AI agents for dermatology
This AI agent performs automated dermoscopy
analysis to identify melanoma and suspicious skin
lesions. It analyzes color patterns, asymmetry, and
border characteristics while providing malignancy
risk scores.
Increases early cancer detection through enhanced
screening accuracy, reduces unnecessary biopsies
via improved risk stratification, and standardizes
skin cancer screening protocols.
This AI copilot provides standardized image
analysis to grade acne severity and track treatment
progress over time. It counts lesions, assesses
inflammation levels, and monitors therapeutic
response objectively.
Optimizes treatment selection through objective
severity assessment, monitors progress objectively
via standardized grading, and improves acne
management outcomes.
Skin cancer detection agent
Acne severity assessment copilot
Psoriasis activity monitoring
assistant
This AI assistant continuously assesses psoriasis
lesion severity using patient-submitted
It tracks disease activity, identifies flare-ups early,
and recommends treatment adjustments based
on progression patterns.
Improves treatment timing through continuous
monitoring, reduces disease flare-ups via early
intervention, and optimizes psoriasis management
through objective assessment.
Drug eruption identifier
This AI agent uses pattern recognition to identify
medication-induced skin reactions from clinical
photographs and patient history.
It correlates timing patterns and reaction
characteristics to identify causative medications.
Accelerates causative drug identification through
systematic analysis, prevents severe reactions via
early recognition, and improves adverse drug
reaction management.
This AI assistant automatically measures wound
dimensions and healing progress from smartphone
photographs. It tracks healing rates, identifies
complications early, and recommends appropriate
wound care protocol adjustments.
Optimizes wound care protocols through objective
monitoring, reduces healing time via early
intervention, and improves wound management
outcomes. Wound healing tracker
This AI copilot performs 3D facial analysis to
optimize aesthetic treatment planning and predict
outcomes. It simulates procedure results and
recommends optimal treatment approaches based
on individual facial anatomy.
Improves patient satisfaction through realistic
outcome prediction, optimizes cosmetic treatment
outcomes via precise planning, and enhances
aesthetic procedure success rates. Cosmetic procedure planner
AI Agents in healthcare 18
AI agents for emergency medicine
This AI agent automatically prioritizes patients
using vital signs, symptoms, and clinical
presentation data. It calculates urgency scores and
optimizes patient flow through emergency
departments for efficient care delivery.
Reduces waiting times through fact-based
prioritization protocols and optimizes emergency
department workflow for better patient handling.
This AI agent continuously monitors vital signs,
laboratory values, and clinical indicators to predict
sepsis onset before clinical deterioration. It
provides early warning alerts for immediate
intervention.
Reduces sepsis mortality through early detection,
enables early intervention via predictive
monitoring, and improves sepsis management
outcomes significantly.
Triage severity predictor
Sepsis early warning agent
This AI agent uses pattern recognition to identify
substances causing overdose symptoms from
clinical presentation and available testing. It
recommends appropriate antidotes and treatment
protocols.
Accelerates antidote administration through rapid
identification, improves overdose survival
outcomes, and enhances toxicological emergency
management.
Drug overdose identification
assistant
This AI copilot provides automated injury scoring
and resource allocation recommendations for
trauma patients. It prioritizes treatment
interventions and optimizes operating room
scheduling based on injury severity.
Improves trauma outcomes through systematic
assessment, optimizes operating room scheduling
via intelligent prioritization, and enhances trauma
care coordination.
This AI assistant provides real-time step-by-step
guidance for emergency procedures, including
intubation, central line placement, and resuscitation
protocols. It offers visual aids and timing
recommendations.
Improves procedure success rates through guided
assistance, reduces procedural complications
via standardized protocols, and enhances
emergency procedure performance.
Trauma assessment copilot
This AI agent rapidly analyzes chest pain
symptoms, ECG findings, and laboratory results to
determine cardiac risk levels. It provides immediate
risk assessment and guides appropriate care
pathways.
Optimizes resource utilization through accurate risk
assessment, reduces unnecessary hospital
admissions, and improves chest pain evaluation
efficiency. Chest pain risk stratification agent
Emergency procedure assistant
AI agents for emergency medicine
This AI agent automatically prioritizes patients
using vital signs, symptoms, and clinical
presentation data. It calculates urgency scores and
optimizes patient flow through emergency
departments for efficient care delivery.
Reduces waiting times through fact-based
prioritization protocols and optimizes emergency
department workflow for better patient handling.
This AI agent continuously monitors vital signs,
laboratory values, and clinical indicators to predict
sepsis onset before clinical deterioration. It
provides early warning alerts for immediate
intervention.
Reduces sepsis mortality through early detection,
enables early intervention via predictive
monitoring, and improves sepsis management
outcomes significantly.
Triage severity predictor
Sepsis early warning agent
This AI agent uses pattern recognition to identify
substances causing overdose symptoms from
clinical presentation and available testing. It
recommends appropriate antidotes and treatment
protocols.
Accelerates antidote administration through rapid
identification, improves overdose survival
outcomes, and enhances toxicological emergency
management.
Drug overdose identification
assistant
This AI copilot provides automated injury scoring
and resource allocation recommendations for
trauma patients. It prioritizes treatment
interventions and optimizes operating room
scheduling based on injury severity.
Improves trauma outcomes through systematic
assessment, optimizes operating room scheduling
via intelligent prioritization, and enhances trauma
care coordination.
This AI assistant provides real-time step-by-step
guidance for emergency procedures, including
intubation, central line placement, and resuscitation
protocols. It offers visual aids and timing
recommendations.
Improves procedure success rates through guided
assistance, reduces procedural complications
via standardized protocols, and enhances
emergency procedure performance.
Trauma assessment copilot
This AI agent rapidly analyzes chest pain
symptoms, ECG findings, and laboratory results to
determine cardiac risk levels. It provides immediate
risk assessment and guides appropriate care
pathways.
Optimizes resource utilization through accurate risk
assessment, reduces unnecessary hospital
admissions, and improves chest pain evaluation
efficiency. Chest pain risk stratification agent
Emergency procedure assistant
This AI copilot continuously monitors risk factors,
including sedation levels, sleep patterns, and
medication effects, to enable early delirium
detection. It provides personalized prevention
strategies and intervention recommendations.
Lessening ICU delirium through predictive risk
assessment improves cognitive outcomes via early
intervention and enhances neurological recovery in
critical patients.
AI Agents in healthcare 19
AI agents for critical care physicians
This AI agent continuously monitors patient lung
mechanics and adjusts ventilator settings in real-
time based on respiratory compliance,
oxygenation, and CO2 levels. It personalizes
ventilation strategies to minimize lung injury.
Reduces ventilator-associated lung injury through
personalized settings, accelerates the weaning
process via optimized protocols, and improves
mechanical ventilation outcomes significantly.
This AI agent provides real-time cardiac output
analysis using multiple hemodynamic parameters to
optimize fluid resuscitation and vasoactive
medication dosing. It integrates pressure
measurements with clinical indicators.
Optimizes fluid management through precise
hemodynamic assessment, reduces multiple organ
dysfunction via targeted interventions, and
improves cardiovascular support strategies.
Mechanical ventilation
optimization agent
Hemodynamic monitoring agent
Delirium prevention copilot
This AI assistant automatically detects healthcare-
associated infections by analyzing laboratory data,
vital signs, and clinical indicators. It identifies
infection patterns and provides antimicrobial
stewardship recommendations.
Lower infection rates through early detection,
optimize antibiotic stewardship programs, and
improve infection control measures in intensive
care settings.
This AI agent analyzes vital signs, laboratory
values, and clinical trends to predict cardiac arrest
risk before clinical deterioration. It provides early
warning alerts for preventive interventions.
Reduces cardiac arrests through predictive
monitoring, improves patient survival rates via early
intervention, and enhances critical care safety
protocols.
This AI agent uses predictive modeling to optimize
bed utilization, staffing requirements, and
equipment allocation based on patient acuity and
census forecasting. It improves operational
efficiency.
Optimizes bed utilization through predictive
analytics, improves patient throughput efficiency,
and enhances ICU operational management for
better resource allocation.
Infection surveillance assistant
Code blue prediction agent
ICU resource allocation agent
including sedation levels, sleep patterns, and
medication effects, to enable early delirium
detection. It provides personalized prevention
strategies and intervention recommendations.
Lessening ICU delirium through predictive risk
assessment improves cognitive outcomes via early
intervention and enhances neurological recovery in
critical patients.
AI Agents in healthcare 19
AI agents for critical care physicians
This AI agent continuously monitors patient lung
mechanics and adjusts ventilator settings in real-
time based on respiratory compliance,
oxygenation, and CO2 levels. It personalizes
ventilation strategies to minimize lung injury.
Reduces ventilator-associated lung injury through
personalized settings, accelerates the weaning
process via optimized protocols, and improves
mechanical ventilation outcomes significantly.
This AI agent provides real-time cardiac output
analysis using multiple hemodynamic parameters to
optimize fluid resuscitation and vasoactive
medication dosing. It integrates pressure
measurements with clinical indicators.
Optimizes fluid management through precise
hemodynamic assessment, reduces multiple organ
dysfunction via targeted interventions, and
improves cardiovascular support strategies.
Mechanical ventilation
optimization agent
Hemodynamic monitoring agent
Delirium prevention copilot
This AI assistant automatically detects healthcare-
associated infections by analyzing laboratory data,
vital signs, and clinical indicators. It identifies
infection patterns and provides antimicrobial
stewardship recommendations.
Lower infection rates through early detection,
optimize antibiotic stewardship programs, and
improve infection control measures in intensive
care settings.
This AI agent analyzes vital signs, laboratory
values, and clinical trends to predict cardiac arrest
risk before clinical deterioration. It provides early
warning alerts for preventive interventions.
Reduces cardiac arrests through predictive
monitoring, improves patient survival rates via early
intervention, and enhances critical care safety
protocols.
This AI agent uses predictive modeling to optimize
bed utilization, staffing requirements, and
equipment allocation based on patient acuity and
census forecasting. It improves operational
efficiency.
Optimizes bed utilization through predictive
analytics, improves patient throughput efficiency,
and enhances ICU operational management for
better resource allocation.
Infection surveillance assistant
Code blue prediction agent
ICU resource allocation agent
AI Agents in healthcare 20
AI agents for pathologists
This AI assistant provides intelligent analysis of
complex laboratory panels with clinical correlation
and trend analysis. It identifies critical values and
ciffmcrcr8ssuosu98rmr :929 8:r8 r9o2ct5
Accelerates diagnosis through automated
interpretation, reduces laboratory interpretation
errors, and improves clinical correlation of complex
laboratory data.
This AI assistant performs automated cancer
detection in tissue specimens using advanced
imaging analysis. It identifies malignant cells,
grades tumors, and provides diagnostic
um o33m2n8r9o2ct5
Standardizes pathology reporting through
consistent analysis, reduces diagnostic turnaround
time, and improves cancer detection accuracy in
tissue specimens.
This AI agent continuously monitors analytical
processes to detect systematic errors, instrument
malfunctions, and quality control failures. It
suo;9nmcrum8:cr93mr8:murcraour ouum r9;mr8 r9o2ct5
Improves test accuracy through continuous
monitoring, reduces false laboratory results, and
enhances laboratory quality assurance programs
significantly.
Automated lab result
interpretation assistant
Digital pathology assistant
Laboratory quality control agent
This AI copilot integrates genomic, proteomic, and
clinical data to identify novel disease biomarkers
and validate diagnostic applications. It accelerates
e9o38u?murumcm8u 7r82nrnm;m:os3m2rt5
Accelerates biomarker development through
integrated analysis, improves diagnostic test
accuracy, and enhances precision medicine
capabilities for personalized patient care.
This AI assistant provides automated pathogen
identification and antimicrobial susceptibility
testing using advanced pattern recognition. It
correlates clinical presentation with microbiological
a92n92fct5
Bring down pathogen identification time through
automated analysis, optimize antibiotic selection,
and improve microbiological diagnostic accuracy
for better patient outcomes.
This AI agent analyzes blood compatibility,
manages inventory optimization, and predicts
transfusion requirements. It ensures safe blood
product allocation and reduces wastage through
sumn9 r9;mr3onm:92ft5
Reduces transfusion reactions through enhanced
compatibility analysis, optimizes blood bank
operational efficiency, and improves transfusion
safety protocols.
Biomarker discovery copilot
Microbiology identification
assistant
Transfusion medicine agent
AI agents for pathologists
This AI assistant provides intelligent analysis of
complex laboratory panels with clinical correlation
and trend analysis. It identifies critical values and
ciffmcrcr8ssuosu98rmr :929 8:r8 r9o2ct5
Accelerates diagnosis through automated
interpretation, reduces laboratory interpretation
errors, and improves clinical correlation of complex
laboratory data.
This AI assistant performs automated cancer
detection in tissue specimens using advanced
imaging analysis. It identifies malignant cells,
grades tumors, and provides diagnostic
um o33m2n8r9o2ct5
Standardizes pathology reporting through
consistent analysis, reduces diagnostic turnaround
time, and improves cancer detection accuracy in
tissue specimens.
This AI agent continuously monitors analytical
processes to detect systematic errors, instrument
malfunctions, and quality control failures. It
suo;9nmcrum8:cr93mr8:murcraour ouum r9;mr8 r9o2ct5
Improves test accuracy through continuous
monitoring, reduces false laboratory results, and
enhances laboratory quality assurance programs
significantly.
Automated lab result
interpretation assistant
Digital pathology assistant
Laboratory quality control agent
This AI copilot integrates genomic, proteomic, and
clinical data to identify novel disease biomarkers
and validate diagnostic applications. It accelerates
e9o38u?murumcm8u 7r82nrnm;m:os3m2rt5
Accelerates biomarker development through
integrated analysis, improves diagnostic test
accuracy, and enhances precision medicine
capabilities for personalized patient care.
This AI assistant provides automated pathogen
identification and antimicrobial susceptibility
testing using advanced pattern recognition. It
correlates clinical presentation with microbiological
a92n92fct5
Bring down pathogen identification time through
automated analysis, optimize antibiotic selection,
and improve microbiological diagnostic accuracy
for better patient outcomes.
This AI agent analyzes blood compatibility,
manages inventory optimization, and predicts
transfusion requirements. It ensures safe blood
product allocation and reduces wastage through
sumn9 r9;mr3onm:92ft5
Reduces transfusion reactions through enhanced
compatibility analysis, optimizes blood bank
operational efficiency, and improves transfusion
safety protocols.
Biomarker discovery copilot
Microbiology identification
assistant
Transfusion medicine agent
AI Agents in healthcare 21
AI agents for physical medicine and rehabilitation
This AI agent analyzes injury severity, patient
demographics, and baseline functional status to
predict rehabilitation potential and recovery
trajectories. It integrates multiple prognostic
factors for comprehensive outcome forecasting.
Optimizes treatment planning through evidence-
based predictions, sets realistic recovery goals for
patients, and improves rehabilitation resource
allocation for better outcomes.
This AI assistant continuously monitors walking
patterns using wearable sensors to analyze stride
length, cadence, and balance parameters. It
provides real-time feedback and personalized gait
training recommendations.
Personalizes rehabilitation protocols through
objective gait assessment, improves mobility
functional outcomes, and enhances walking
recovery in neurological and orthopedic patients.
This AI assistant provides guidance for prosthetic
selection and fitting based on residual limb
anatomy, activity level, and functional goals. It
optimizes prosthetic alignment and interface
design.
Improves prosthetic function through personalized
fitting algorithms, enhances amputee quality
of life, and optimizes prosthetic prescription for
individual patient needs.
Functional outcome prediction
agent
Gait analysis assistant
Prosthetic fitting assistant
This AI copilot performs automated spasticity
assessment using sensor-based measurements
and provides personalized treatment
recommendations including medication dosing and
therapy interventions for optimal spasticity control.
Optimizes medication timing through objective
spasticity monitoring, improves motor function
recovery, and enhances spasticity management in
neurological rehabilitation patients.
This AI assistant continuously monitors exercise
intensity, heart rate response, and cardiac
parameters during rehabilitation sessions. It
provides real-time safety alerts and personalized
exercise prescription adjustments.
Personalizes exercise prescriptions through cardiac
monitoring, prevents cardiac rehabilitation events,
and optimizes cardiovascular recovery in cardiac
rehabilitation programs.
This AI agent optimizes brain stimulation protocols
based on neuroimaging data and functional
assessments. It personalizes stimulation
parameters to maximize neuroplasticity and
accelerate neural recovery.
Accelerates recovery through optimized brain
stimulation, maximizes brain plasticity
rehabilitation potential, and enhances neurological
rehabilitation outcomes in stroke patients.
Spasticity management copilot
Cardiac rehabilitation monitoring
assistant
Neuroplasticity enhancement
agent
AI agents for physical medicine and rehabilitation
This AI agent analyzes injury severity, patient
demographics, and baseline functional status to
predict rehabilitation potential and recovery
trajectories. It integrates multiple prognostic
factors for comprehensive outcome forecasting.
Optimizes treatment planning through evidence-
based predictions, sets realistic recovery goals for
patients, and improves rehabilitation resource
allocation for better outcomes.
This AI assistant continuously monitors walking
patterns using wearable sensors to analyze stride
length, cadence, and balance parameters. It
provides real-time feedback and personalized gait
training recommendations.
Personalizes rehabilitation protocols through
objective gait assessment, improves mobility
functional outcomes, and enhances walking
recovery in neurological and orthopedic patients.
This AI assistant provides guidance for prosthetic
selection and fitting based on residual limb
anatomy, activity level, and functional goals. It
optimizes prosthetic alignment and interface
design.
Improves prosthetic function through personalized
fitting algorithms, enhances amputee quality
of life, and optimizes prosthetic prescription for
individual patient needs.
Functional outcome prediction
agent
Gait analysis assistant
Prosthetic fitting assistant
This AI copilot performs automated spasticity
assessment using sensor-based measurements
and provides personalized treatment
recommendations including medication dosing and
therapy interventions for optimal spasticity control.
Optimizes medication timing through objective
spasticity monitoring, improves motor function
recovery, and enhances spasticity management in
neurological rehabilitation patients.
This AI assistant continuously monitors exercise
intensity, heart rate response, and cardiac
parameters during rehabilitation sessions. It
provides real-time safety alerts and personalized
exercise prescription adjustments.
Personalizes exercise prescriptions through cardiac
monitoring, prevents cardiac rehabilitation events,
and optimizes cardiovascular recovery in cardiac
rehabilitation programs.
This AI agent optimizes brain stimulation protocols
based on neuroimaging data and functional
assessments. It personalizes stimulation
parameters to maximize neuroplasticity and
accelerate neural recovery.
Accelerates recovery through optimized brain
stimulation, maximizes brain plasticity
rehabilitation potential, and enhances neurological
rehabilitation outcomes in stroke patients.
Spasticity management copilot
Cardiac rehabilitation monitoring
assistant
Neuroplasticity enhancement
agent
AI Agents in healthcare 22
5
A decision framework for
Gen AI implementations
When implementing AI copilots for your healthcare organization,
the abundance of available solutions can make the decision
process overwhelming. A structured evaluation framework helps
hospital leaders assess which AI copilot aligns best with their
specific clinical workflows, technical infrastructure, and strategic
objectives. The right choice requires balancing immediate
functionality needs with long-term scalability and integration
capabilities.
5
A decision framework for
Gen AI implementations
When implementing AI copilots for your healthcare organization,
the abundance of available solutions can make the decision
process overwhelming. A structured evaluation framework helps
hospital leaders assess which AI copilot aligns best with their
specific clinical workflows, technical infrastructure, and strategic
objectives. The right choice requires balancing immediate
functionality needs with long-term scalability and integration
capabilities.
AI Agents in healthcare 23
We propose a simplified assessment framework for
pilots and proof of concepts, which combines
outcomes, technology, and vendor:
1 Clinical accuracy:
1 Integration capabilities:
1 Scalability:
1 Support structure:
1 Vendor:
Evidence-based validation,
peer-reviewed studies]
EHR compatibility,
workflow seamlessness.f
Growth accommodation, multi-site
deployment]
24/7 availability, clinical
expertise]
Comprehensive vendor selection
exercise.
Assessment matrix
1 Proven track record:
1 Regulatory compliance:
1 Implementation speed:
1 Ongoing support:
1 Partnerships:
Real-world case studies
and outcomes]
FDA approvals, HIPAA
certification]
Time to value, minimal
disruption]
Continuous updates, clinical
consultation.f
Deep expertise and partnerships
with Gen AI ecosystem.
The biggest factor that can make or break your
pilots is the vendor. In all likelihood, you would need
a Gen AI consulting and development partner to
recommend the right approach to testing AI within
your settings. Look for:
Build/Buy: Key evaluation
criteria for vendors
To summarize, an ideal AI consulting and
development partner must have:
Vendor selection checklist
✓
✓
✓
✓
✓
Healthcare-specific expertise and domain
knowledge.
Enterprise-grade security and compliance
frameworks.
Rapid deployment capabilities for pilots.
Measurable ROI and outcome tracking
frameworks.
24/7 support and continuous optimization.
We propose a simplified assessment framework for
pilots and proof of concepts, which combines
outcomes, technology, and vendor:
1 Clinical accuracy:
1 Integration capabilities:
1 Scalability:
1 Support structure:
1 Vendor:
Evidence-based validation,
peer-reviewed studies]
EHR compatibility,
workflow seamlessness.f
Growth accommodation, multi-site
deployment]
24/7 availability, clinical
expertise]
Comprehensive vendor selection
exercise.
Assessment matrix
1 Proven track record:
1 Regulatory compliance:
1 Implementation speed:
1 Ongoing support:
1 Partnerships:
Real-world case studies
and outcomes]
FDA approvals, HIPAA
certification]
Time to value, minimal
disruption]
Continuous updates, clinical
consultation.f
Deep expertise and partnerships
with Gen AI ecosystem.
The biggest factor that can make or break your
pilots is the vendor. In all likelihood, you would need
a Gen AI consulting and development partner to
recommend the right approach to testing AI within
your settings. Look for:
Build/Buy: Key evaluation
criteria for vendors
To summarize, an ideal AI consulting and
development partner must have:
Vendor selection checklist
✓
✓
✓
✓
✓
Healthcare-specific expertise and domain
knowledge.
Enterprise-grade security and compliance
frameworks.
Rapid deployment capabilities for pilots.
Measurable ROI and outcome tracking
frameworks.
24/7 support and continuous optimization.
AI Agents in healthcare 24
6
Gen AI playbook for
healthcare providers
GoML is a leading AI consulting and development partner for
healthcare providers around the world. Based on our own
implementations, we have built a comprehensive framework that
will take you from your current state to Gen AI ready very quickly.
6
Gen AI playbook for
healthcare providers
GoML is a leading AI consulting and development partner for
healthcare providers around the world. Based on our own
implementations, we have built a comprehensive framework that
will take you from your current state to Gen AI ready very quickly.
AI Agents in healthcare 25
Initial assessment and planning (weeks 1-2)
Phase 1:
; Conduct comprehensive workflow assessment
across departments3
; Identify top 3-5 pain points impacting clinical
efficiency3
; Document existing technology infrastructure
and integration capabilities3
; Assess staff readiness and change
management requirements.
; Rank opportunities by clinical impact and ROI
potential3
; Focus on high-volume, repetitive tasks with
clear success metrics3
; Consider regulatory compliance requirements
for each use case3
; Align priorities with organizational strategic
objectives.
Current state analysis
Use case prioritization
Technical planning
; Define integration requirements with existing
EHR systems3
; Establish data governance and security
protocols3
; Plan infrastructure needs for AI deployment3
; Set up project governance and stakeholder
communication.
Initial assessment and planning (weeks 1-2)
Phase 1:
; Conduct comprehensive workflow assessment
across departments3
; Identify top 3-5 pain points impacting clinical
efficiency3
; Document existing technology infrastructure
and integration capabilities3
; Assess staff readiness and change
management requirements.
; Rank opportunities by clinical impact and ROI
potential3
; Focus on high-volume, repetitive tasks with
clear success metrics3
; Consider regulatory compliance requirements
for each use case3
; Align priorities with organizational strategic
objectives.
Current state analysis
Use case prioritization
Technical planning
; Define integration requirements with existing
EHR systems3
; Establish data governance and security
protocols3
; Plan infrastructure needs for AI deployment3
; Set up project governance and stakeholder
communication.
AI Agents in healthcare 26
Pilot program design (weeks 3-6)
Phase 2:
P Select the right enterprise-grade LLM
boilerplate for rapid deployment5
P Customize AI copilot functionality for specific
clinical workflows5
P Configure natural language processing for
healthcare terminology5
P Develop real-time data integration capabilities.
P Create user training programs for clinical staff5
P Establish feedback loops for continuous
improvement5
P Design adoption metrics and success tracking
systems5
P Plan communication strategy for organization-
wide rollout.
P Deploy pilot in limited clinical environment5
P Test with real patient data under strict security
protocols5
P Validate clinical outcomes and operational
efficiency gains5
P Gather user feedback and iterate on solution
design5
P Acceptance and RoI analysis.
Solution development
Training and change management
Controlled deployment
Pilot program design (weeks 3-6)
Phase 2:
P Select the right enterprise-grade LLM
boilerplate for rapid deployment5
P Customize AI copilot functionality for specific
clinical workflows5
P Configure natural language processing for
healthcare terminology5
P Develop real-time data integration capabilities.
P Create user training programs for clinical staff5
P Establish feedback loops for continuous
improvement5
P Design adoption metrics and success tracking
systems5
P Plan communication strategy for organization-
wide rollout.
P Deploy pilot in limited clinical environment5
P Test with real patient data under strict security
protocols5
P Validate clinical outcomes and operational
efficiency gains5
P Gather user feedback and iterate on solution
design5
P Acceptance and RoI analysis.
Solution development
Training and change management
Controlled deployment
AI Agents in healthcare 27
Full-scale implementation (weeks 7-8)
Phase 3:
N Execute phased deployment across all
departments<
N Monitor system performance and user adoption
rates<
N Provide real-time support and troubleshooting<
N Optimize workflows based on initial deployment
learnings.
N Track key metrics: documentation time,
diagnostic accuracy, decision speed<
N Measure cost reduction and operational
efficiency improvements<
N Monitor user satisfaction and adoption rates<
N Document clinical outcome improvements.
N Implement feedback-driven enhancements<
N Scale successful use cases to additional
departments<
N Plan for future AI capabilities and feature
additions<
N Establish long-term partnership and support
structure.
Organization-wide rollout
Performance monitoring
Continuous optimization
Full-scale implementation (weeks 7-8)
Phase 3:
N Execute phased deployment across all
departments<
N Monitor system performance and user adoption
rates<
N Provide real-time support and troubleshooting<
N Optimize workflows based on initial deployment
learnings.
N Track key metrics: documentation time,
diagnostic accuracy, decision speed<
N Measure cost reduction and operational
efficiency improvements<
N Monitor user satisfaction and adoption rates<
N Document clinical outcome improvements.
N Implement feedback-driven enhancements<
N Scale successful use cases to additional
departments<
N Plan for future AI capabilities and feature
additions<
N Establish long-term partnership and support
structure.
Organization-wide rollout
Performance monitoring
Continuous optimization
AI Agents in healthcare 28
How we support your AI journey
B Workflow analysis: identify highest-impact
opportunities within your organization#
B Technical assessment: evaluate integration
requirements and infrastructure needs#
B Use case design: create tailored solutions for
your specific clinical challenges#
B ROI modeling: calculate tangible benefits and
investment returns.
B Build working prototypes with real data
integration#
B Demonstrate measurable value through
workflow simulation#
B Provide outcome measurement and success
validation#
B Ensure compliance with healthcare regulations
and standards.
B Ongoing optimization and feature updates.t
B Clinical consultation for sustained value
delivery#
B Performance monitoring and outcome tracking.t
B Continuous improvement and scaling support.
Discovery workshop process
Proof of concept development
Partnership and support model
How we support your AI journey
B Workflow analysis: identify highest-impact
opportunities within your organization#
B Technical assessment: evaluate integration
requirements and infrastructure needs#
B Use case design: create tailored solutions for
your specific clinical challenges#
B ROI modeling: calculate tangible benefits and
investment returns.
B Build working prototypes with real data
integration#
B Demonstrate measurable value through
workflow simulation#
B Provide outcome measurement and success
validation#
B Ensure compliance with healthcare regulations
and standards.
B Ongoing optimization and feature updates.t
B Clinical consultation for sustained value
delivery#
B Performance monitoring and outcome tracking.t
B Continuous improvement and scaling support.
Discovery workshop process
Proof of concept development
Partnership and support model
AI Agents in healthcare 29
Success metrics framework
# Diagnostic accuracy improvements4
# Clinical decision-making speed4
# Patient safety enhancements4
# Quality of care indicators.
# Documentation time reduction4
# Workflow optimization gains4
# Staff productivity improvements4
# Resource utilization optimization.
# Operational cost reduction4
# Revenue cycle improvements4
# Error prevention savings4
# ROI achievement and sustainability.
Clinical outcomes
Operational efficiency
Financial impact
Success metrics framework
# Diagnostic accuracy improvements4
# Clinical decision-making speed4
# Patient safety enhancements4
# Quality of care indicators.
# Documentation time reduction4
# Workflow optimization gains4
# Staff productivity improvements4
# Resource utilization optimization.
# Operational cost reduction4
# Revenue cycle improvements4
# Error prevention savings4
# ROI achievement and sustainability.
Clinical outcomes
Operational efficiency
Financial impact
AI Agents in healthcare 30
7
Ready to transform your
healthcare practice?
GoML has worked with providers around the world to solve the
problems of physician burnout and clinical workflow efficiency. We
share some of our customer stories below:
7
Ready to transform your
healthcare practice?
GoML has worked with providers around the world to solve the
problems of physician burnout and clinical workflow efficiency. We
share some of our customer stories below:
AI Agents in healthcare 31
Max healthcare—longitudinal patient data revolution
Featured case study:
Challenge:
Solution:
Results:
Impact:
Clinicians and analysts struggled to
extract insights from vast volumes of patient data
stored across multiple sources. Accessing clinical
findings from longitudinal patient data often
required backend intervention, leading to long
delays and fragmented workflows.
GoML designed and built a generative AI
copilot, leveraging Claude 3.5 on AWS Bedrock for
natural language understanding and reasoning.
Real-time clinical decision-making,
proactive chronic condition management
GoML helped Max shift from reactive to
proactive care, giving doctors instant access to the
data that matters, and transforming how they treat
patients.
Max healthcare—longitudinal patient data revolution
Featured case study:
Challenge:
Solution:
Results:
Impact:
Clinicians and analysts struggled to
extract insights from vast volumes of patient data
stored across multiple sources. Accessing clinical
findings from longitudinal patient data often
required backend intervention, leading to long
delays and fragmented workflows.
GoML designed and built a generative AI
copilot, leveraging Claude 3.5 on AWS Bedrock for
natural language understanding and reasoning.
Real-time clinical decision-making,
proactive chronic condition management
GoML helped Max shift from reactive to
proactive care, giving doctors instant access to the
data that matters, and transforming how they treat
patients.
AI Agents in healthcare 32
Atria Healthcare—intelligent patient profiling
Case study 2:
Challenge:
Solution:
Results:
Impact:
Patient onboarding times were as high
as 3 – 4 months, which was a major roadblock
towards scaling their subscription based proactive
care approach. Atria was reaching a limit to the
number of patients they could effectively treat and
predict risks for.
AI-powered multi-agent framework that
efficiently processes decades of patient history to
assist healthcare providers in real-time.
Processes 20–30 years of patient history
within seconds, creating comprehensive patient
summaries.
Helped Atria save a 9 year old's life, with
historical data analysis and insights generation
within seconds, to identify life threatening
condition.
Atria Healthcare—intelligent patient profiling
Case study 2:
Challenge:
Solution:
Results:
Impact:
Patient onboarding times were as high
as 3 – 4 months, which was a major roadblock
towards scaling their subscription based proactive
care approach. Atria was reaching a limit to the
number of patients they could effectively treat and
predict risks for.
AI-powered multi-agent framework that
efficiently processes decades of patient history to
assist healthcare providers in real-time.
Processes 20–30 years of patient history
within seconds, creating comprehensive patient
summaries.
Helped Atria save a 9 year old's life, with
historical data analysis and insights generation
within seconds, to identify life threatening
condition.
AI Agents in healthcare 33
Next-gen retinal imaging innovation
Case study 3:
Challenge:
Solution:
Results:
Impact:
Current systems at the hospital failed to
provide early insights required for faster clinical
decision-making. These limitations meant every
retinal scan required a doctor’s review, making it
impossible to scale.
AI-powered retinal imaging analysis that
significantly speeded up diagnosis of potential
conditions like glaucoma, marking high risk scans
for doctors’ review, along with vital information.
Improved diagnostic speed, accuracy, and
treatment outcomes for diabetic retinopathy and
other retinal conditions.
Scalable retinal screening with faster
clinical decision-making and escalation workflows.
This is just a sample of the work that has happened
around the healthcare world with Gen AI.
GoML has a strong suite of case studies,
boilerplates, and playbooks to help you on your
clinical copilot journey.
Next-gen retinal imaging innovation
Case study 3:
Challenge:
Solution:
Results:
Impact:
Current systems at the hospital failed to
provide early insights required for faster clinical
decision-making. These limitations meant every
retinal scan required a doctor’s review, making it
impossible to scale.
AI-powered retinal imaging analysis that
significantly speeded up diagnosis of potential
conditions like glaucoma, marking high risk scans
for doctors’ review, along with vital information.
Improved diagnostic speed, accuracy, and
treatment outcomes for diabetic retinopathy and
other retinal conditions.
Scalable retinal screening with faster
clinical decision-making and escalation workflows.
This is just a sample of the work that has happened
around the healthcare world with Gen AI.
GoML has a strong suite of case studies,
boilerplates, and playbooks to help you on your
clinical copilot journey.
AI Agents in healthcare 34
Next steps
5 Schedule discovery workshop:
5 Get custom ROI analysis:
5 Start 8-week pilot:
5 Plan full implementation:
Assess your organization's AI
readinessI
Understand your specific investment
returnsI
Begin with controlled deployment and
validationI
Scale successful pilots organization-
wide.
Funding support for Gen AI
GoML understands the ground reality of piloting and scaling Gen AI
for enterprises very well. That’s why we focus on building systems
that work inside your enterprise workflows. It is necessary for
enterprises that can’t afford to waste another quarter in POC limbo.
By partnering with us, you can gain access to special funding
programs that are specifically designed for healthcare Gen AI use
cases. You can run lean pilots, validate your concept in weeks and
get enterprise-grade infrastructure without the upfront cost. Once
this is complete, and you wish to scale, we will equip you with the
blueprint for the architecture, partnerships and support that will
take you where you need to go.
If you are serious about Gen AI, GoML offers a clear path to impact,
with access to funding programs and a strategy to turn that
investment into real results.
Next steps
5 Schedule discovery workshop:
5 Get custom ROI analysis:
5 Start 8-week pilot:
5 Plan full implementation:
Assess your organization's AI
readinessI
Understand your specific investment
returnsI
Begin with controlled deployment and
validationI
Scale successful pilots organization-
wide.
Funding support for Gen AI
GoML understands the ground reality of piloting and scaling Gen AI
for enterprises very well. That’s why we focus on building systems
that work inside your enterprise workflows. It is necessary for
enterprises that can’t afford to waste another quarter in POC limbo.
By partnering with us, you can gain access to special funding
programs that are specifically designed for healthcare Gen AI use
cases. You can run lean pilots, validate your concept in weeks and
get enterprise-grade infrastructure without the upfront cost. Once
this is complete, and you wish to scale, we will equip you with the
blueprint for the architecture, partnerships and support that will
take you where you need to go.
If you are serious about Gen AI, GoML offers a clear path to impact,
with access to funding programs and a strategy to turn that
investment into real results.
Schedule a free executive
AI Briefing
We provide complimentary discovery consultations to identify your
highest-impact AI opportunities and create a customized
implementation roadmap for your organization. We will also answer
questions about AI for your executives. Schedule a call now.
Schedule: goml.io/demo Website: www.goml.io
Contact information:
AI Briefing
We provide complimentary discovery consultations to identify your
highest-impact AI opportunities and create a customized
implementation roadmap for your organization. We will also answer
questions about AI for your executives. Schedule a call now.
Schedule: goml.io/demo Website: www.goml.io
Contact information:
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