AI in Practice for Healthcare

AI in Healthcare - transforming clincial practice, teahcing and research.

1. From Scalpel to Algorithm How AI is Revolutionizing Medical Education, Research and Clinical Practice Vaikunthan Rajaratnam Hand Surgeon, Medical Educator and Instructional Designer

2. Disclaimer I am not an AI expert , nor do I possess coding knowledge specific to the underlying mechanisms of AI models; my expertise lies in the utilisation of these models , such as ChatGPT, based on my extensive experience as a user within the fields of healthcare, medical education, and related research, rather than their technical development or underlying algorithms .

3. Introduction to AI in Healthcare: Opportunities and Challenges AI technologies have the potential to revolutionize healthcare by enhancing diagnosis, treatment planning, and research. AI won't replace you, but someone empowered by AI undoubtedly will

4. Understanding AI, Generative AI, and ChatGPT AI (Artificial Intelligence) refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions Applications: Includes machine learning, natural language processing, robotics, computer vision, etc. Generative AI subset of AI that focuses on creating new data instances that are similar to a set of training examples. Techniques: Examples include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), etc. ChatGPT ( Generative Pretrained Transformer ) : State-of-the-art language models developed by OpenAI. It utilises the Transformer architecture to generate human-like text based on given prompts. Usage: Widely used in natural language understanding tasks, chatbots, content creation, and more.

5. Suero-Abreu, G. A., Hamid, A., Akbilgic , O., & Brown, S.-A. (2022 ). Trends in cardiology and oncology artificial intelligence publications. American Heart Journal Plus: Cardiology Research and Practice , 17 , 100162. https://doi.org/10.1016/j.ahjo.2022.100162

6. Rapid multi-disciplinary stream of authors researching AI in Medicine   Skills and data quality awareness for data-intensive analysis Limitations Ethics, Data governance , and C ompetencies of the health workforce . Focuses on H ealth services management P redictive medicine P atient data and diagnostics C linical decision-making Secinaro , S., Calandra, D., Secinaro , A., Muthurangu , V., & Biancone, P. (2021 ). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making , 21 (1), 125. https://doi.org/10.1186/s12911-021-01488-9

7. H ealth services management Optimization of Operational Efficiency Example: Scheduling algorithms to optimize staff shifts and patient appointments, reducing wait times. Predictive Analytics for Resource Allocation Example: Predicting hospital bed occupancy based on patient flow and admission trends for better resource planning. Supply Chain Optimization Example: Forecasting the need for medical supplies and automating procurement to reduce inventory costs. Fraud Detection and Compliance Example: Detecting fraudulent billing activities and ensuring compliance with healthcare regulations. Integration of Care across Providers Example: Facilitating seamless information sharing among healthcare providers for coordinated care. Enhancing Administrative Decision-Making Example: Utilizing data analytics to inform strategic decisions, such as facility expansion or service prioritization. Patient Engagement and Communication Example: AI-powered chatbots to handle routine inquiries, appointment scheduling, and patient follow-ups. Workforce Development and Training Example: Using AI to identify training needs and deliver personalized learning paths for healthcare staff. Performance Monitoring and Quality Assurance Example: Implementing AI-driven analytics to monitor performance metrics, identify areas for improvement, and ensure quality standards. Cost Control and Optimization Example: Applying AI to analyze cost drivers, identify inefficiencies, and recommend cost-saving measures.

8. P redictive medicine Early Disease Detection Example: Using AI algorithms to analyze medical imaging for early detection of cancers, even before symptoms appear. Risk Stratification Example: Identifying patients at high risk of chronic conditions like heart disease based on a combination of genetic, lifestyle, and clinical data. Personalized Treatment Plans Example: Creating tailored treatment regimens by predicting individual responses to specific drugs or therapies. Epidemic Outbreak Prediction Example: Analyzing social media, travel patterns, and other data sources to predict the spread of infectious diseases like flu or COVID-19. Hospital Readmission Prediction Example: Determining the likelihood of a patient's readmission to the hospital, allowing for targeted interventions to reduce readmissions. Drug Response Prediction Example: Predicting how individual patients will respond to certain medications, minimizing adverse effects, and improving treatment efficacy. Genomic Medicine and Genetic Risk Prediction Example: Analyzing genetic data to predict susceptibility to genetic disorders and guide preventive measures. Mental Health Outcome Prediction Example: Utilizing AI to predict mental health crises or progression of conditions like depression based on patient behavior and medical history. Chronic Disease Management Example: Continuous monitoring and prediction of disease progression in chronic conditions like diabetes, allowing for timely interventions.

9. Patient data and diagnostics Automated Data Analysis and Interpretation Example : Using AI to analyze complex laboratory results, such as genetic sequencing, to identify patterns and anomalies. Real-Time Monitoring and Alerting Example : Continuously tracking vital signs and alerting medical staff to potential issues, such as deterioration in a patient's condition. Enhanced Medical Imaging Interpretation Example : Applying AI algorithms to interpret radiological images, such as X-rays and MRIs, with increased accuracy and speed. Predictive Analytics for Personalized Care Example : Analyzing patient data to predict individual responses to treatments, enabling more personalized and effective care plans. Data Integration and Holistic Patient Views Example : Aggregating data from various sources (e.g., EMRs, wearables) to provide a comprehensive view of a patient's health status. Telemedicine and Remote Diagnostics Example : Utilizing AI-powered tools to diagnose and manage patients in remote locations, increasing healthcare accessibility. Natural Language Processing for Clinical Notes Example : Extracting valuable information from unstructured clinical notes through AI, enhancing data usability. Genomic and Precision Medicine Example : Integrating genomic data with clinical information to provide precise diagnoses and personalized treatment recommendations. Chronic Condition Management and Monitoring Example : Using AI to diagnose and monitor chronic conditions, such as diabetes, through continuous data analysis. Ethical and Security Considerations in Data Handling Example : Implementing AI-driven security protocols to ensure patient data privacy and compliance with regulations.

10. Clinical decision-making Evidence-Based Recommendations Example : AI systems can analyze vast medical literature to provide evidence-based treatment recommendations tailored to individual patient profiles. Diagnostic Support Tools Example : AI algorithms can assist physicians in diagnosing complex conditions by analyzing clinical data, medical imaging, and laboratory results. Predicting Patient Outcomes Example : Using AI to predict patient responses to various treatments, aiding in selecting the most effective therapy. Treatment Pathway Optimization Example : AI can suggest optimal treatment pathways based on patient characteristics, medical history, and current clinical guidelines. Enhancing Multidisciplinary Collaboration Example : AI-driven platforms can facilitate collaboration among specialists, integrating insights from various disciplines for comprehensive care. Ethical Considerations in Decision Making Example : Implementing AI algorithms that consider ethical principles, such as fairness and transparency, in clinical decisions.

11. Challenges D ata T rust E thics R eadiness for change, Expertise B uy-in R egulatory strategy Scalability E valuation Golhar , S. P., & Kekapure , S. S. (2022). Artificial Intelligence in Healthcare—A Review. International Journal of Scientific Research in Science and Technology , 9 (4), 381–387. https://doi.org/10.32628/IJSRST229454

12. Governance Model for AI S. Reddy, S. Allan, S. Coghlan, and P. Cooper, ‘A governance model for the application of AI in health care’, J. Am. Med. Inform. Assoc. , vol. 27, no. 3, pp. 491–497, Mar. 2020, doi : 10.1093/ jamia /ocz192 Rahman, N., Thamotharampillai , T., & Rajaratnam, V. (2023 ). Ethics, guidelines, and policy for technology in healthcare. In Medical Equipment Engineering: Design, Manufacture and Applications (pp. 119–147). IET Digital Library. https://doi.org/10.1049/PBHE054E_ch9

13. Higgins, D., & Madai, V. I. (2020). From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare. Advanced Intelligent Systems , 2 (10), 2000052. https://doi.org/10.1002/aisy.202000052

14. What is ChatGPT ? Understanding Language Reads and comprehends human-written text. Generating Text Writes human-like text, from answers to creative content. Conversation Capable of engaging in text-based conversations with users. Applications Used in virtual assistants, education, content creation, and more. Not a Human Generates text through algorithms, without feelings or consciousness. AI for Clinical Decision-Making and Patient Care

15. How Do es ChatGPT Work? “ Don’t cry ………..” “ Don’t cry over….” Reading Text : Takes in words, questions, or sentences as input. Understands the language like a human reading a book. Processing Information : Breaks down the input into smaller parts to understand the meaning. Uses a complex mathematical model to analyse the text. Generating Response : Constructs a response based on what it has "learned" from reading lots of text. Tries to make the response sound like something a human would say. No Personal Knowledge or Opinions : Doesn't have thoughts, feelings, or personal experiences. Answers are based on patterns in the data it was trained on, not personal beliefs or opinions. Learning from Data : Trained on a vast amount of text from books, websites, and other written materials. Learns the structure of language and how to create sentences that make sense. Versatility : Can be used for various tasks like answering questions, writing stories, or helping with homework. Adaptable to different subjects and contexts. Not Perfect : Can make mistakes or provide incorrect information. Needs to be used with caution, especially for critical or sensitive topics

16. Understanding ChatGPT A dvanced language model developed by OpenAI. G enerates human-like text based on the prompts. Q uality vs prompt. Quality of Response ∝ Quality of Prompt × Model Understanding Here: Quality of Response is the measure of how relevant, accurate, and coherent the response is. Quality of Prompt represents the clarity, specificity, and relevance of the prompt given to the model. Model Understanding , model's ability to interpret the prompt, including its training, design, and current context.

17. Prompt Generation

18. Prompt Engineering Define the Objective : Identify the specific information or assistance Be Clear and Precise : Use clear language and avoid ambiguity. Include essential details without over-complicating the prompt. Consider Context : Provide relevant background or context to guide the response. Set the Tone and Style : Specify the desired tone (formal, casual) or style (e.g., summary, explanation) if it matters for your use case. Ask Direct Questions : If seeking specific information, formulate your prompt as a direct question. Self Reflective Avoid Bias and Leading Questions : Craft the prompt neutrally to prevent biased or skewed responses. Test and Refine : Experiment with different phrasings and observe how slight changes can affect the response. Refine the prompt Consider Ethical and Privacy Concerns : Ethical guidelines and does not request or reveal sensitive or private information.

19. Response Validation Review response - meets your requirements. No access to real-time data Vaildate Validate Validate . Prompt – response -refine - reprompt .

20. 67-year-old male has dizziness every time he sits up from a lying position, especially in the morning. Also, when he suddenly moves his head, he notes the dizziness. What is the diagnosis

21. What Are Chatbots?

22. Confidentiality and Compliance : Ensure that all interactions are secure and compliant with healthcare regulations .

23. Overcoming Bias Anglocentrism Contextual Understanding Translation Limitations Data Imbalance

24. Relevance to healthcare education

25. Personalized Learning

26. Act like a virtual patient and provide me symptoms and history so that I can improve my clinical skills

27. I have been asked to create a module for the examination of the abdomen for organomegaly for medical students . Create a curriculum and include learning outcomes and the pedagogy and a lesson plan

28. Create an assessment task and provide rubrics for the assessment

29. https://creator.nightcafe.studio/

30. E ducational videos B e concise M obile-compatible Optimized for social media E nhance blended learning A verage view time of 1.72 min ( 103 Seconds )

31. AI for Video Production

32. Write a script for the introduction of the anatomy of the organomegaly medical student module. This will be a 90 second video script. Just provide the narration

33. AI generated Instructional Video

34. Assessment and Feedback Automated Grading : Grading objective assessments (multiple-choice, fill-in-the-blank, etc.) Evaluating subjective assessments (short answers, essays) with predefined criteria Personalized Feedback : Providing tailored feedback on strengths and areas for improvement Engaging in interactive dialogues to reinforce learning concepts Real-time Support : Offering instant feedback on performance Available 24/7 for flexible learning schedules Data-Driven Insights : Tracking performance over time for individual and class insights Designing adaptive learning paths based on student needs Enhancing Human Interaction : Freeing up educators' time for complex student interactions Facilitating structured peer review processes Ethical and Bias Considerations : Ensuring transparency, fairness, and avoidance of biases in AI-driven assessments

35. What are the antibiotics for leprosy treatment

36. Based on this question and answer, create a rubrics to mark answers to the question

37. “the antibiotics used in leprosy are rifampicin and streptomycin. Sometimes you can use dapsone for resistant cases. Rifampicin is the first line drug” - based on this answer provide a grade for it

38. AI Tools for RESEARCH Elicit for Literature Search Scholarcy and Typeset for data extraction and summary Genei.io for summarisation and key points highlighting Keyword generation with ChatGPT ( targeted prompt engineering)

39. Elicit.org

40. Typeset.io