1. Artificial Intelligence in My practice!
Real or Not?
Vaikunthan Rajaratnam
Senior Consultant Hand Surgeon, KTPH, Singapore,
Adjunct Professor & UNESCO Chair Partner,
Asia Pacific University of Technology and Innovation, Malaysia.
28 September 2023

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. AI for Academic Writing Workshop
Write your paper in a day!
8 July 2023
Asia Pacific University of Technology & Innovation (APU)
Malaysia

4. AI for Learning Design
26 August 2023
Asia Pacific University of Technology & Innovation (APU)

5. AI in Healthcare: Unleashing the Power in a
One-Day Workshop
Empowering Healthcare Professionals to
Leverage AI in Practice
7 September 2023

6. Sengkang General
Hospital

7. Introduction to AI in
Healthcare:
Opportunities and
Challenges
AI technologies have the potential
to revolutionize healthcare by
enhancing diagnosis, treatment
planning, and research.

8. 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.

9. 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

10. • Rapid multi-disciplinary
stream of authors
researching AI in Medicine
• Skills and data quality
awareness for data-
intensive analysis
• Limitations
• Ethics,
• Data governance, and
• Competencies of the health
workforce.
• Focuses on
• Health services
management
• Predictive medicine
• Patient data and diagnostics
• Clinical 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

11. Health
services
managemen
t
• 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.

12. Predictiv
e
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.

13. 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.

14. 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

15. Challenges
• Data
• Trust
• Ethics
• Readiness for change,
• Expertise
• Buy-in
• Regulatory strategy
• Scalability
• Evaluation
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

16. 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

17. 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

18. 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

19. How Does
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
opinions.
• Learning from Data:
• Trained on a vast amount of text from books, websites, and other written materia
• 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
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

20. Mastering ChatGPT:
Prompt Generation
and Response
Validation
Harness the power of AI for better interactions
and outcomes
AI is not some monstrous job either. It's simply
the latest result of humanity's long-standing
distaste for boredom. Whether the watermills of
millennial past, or the today's lecture room
mechanics, plentiful evidence points to
humanity's long history of creating a paradise to
undertake a repetitive work.

21. Understanding ChatGPT
• Advanced language
model developed by
OpenAI.
• Generates human-like
text based on the
prompts.
• Quality 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.

22. Prompt Generation
Review
prompt
Crafting a
good
prompt
Clear and
Specific.
Specify
type of
Response

23. 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.

24. Bad Prompts Comments Good Prompts Comments
Tell me about heart
problems.
Too vague, lacks
focus and context.
Summarize the diagnostic criteria for
Congestive Heart Failure according to
the latest ACC/AHA guidelines.
Specific, focused, and
references a reputable
source.
What drugs are good
for high BP, diabetes,
and heart issues?
Overly complex,
risks dangerous
oversimplification.
List the first-line antihypertensive
medications according to the latest
guidelines.
Focused on a single
condition, asks for
evidence-based
treatment.
What's the best
treatment for a 45-
year-old male named
John Smith with
these symptoms?
Contains potentially
identifiable
information, risking
patient
confidentiality.
What are the treatment options for a
45-year-old male presenting with
these generic symptoms?
Generalized and
anonymized,
preserving patient
confidentiality.

25. Response Validation
• Review response - meets your requirements.
• No access to real-time data
• Vaildate Validate Validate.
• Prompt – response -refine - reprompt.
Relevance Check
Accuracy
Confirmation
Context
Consistency
Sensitivity Review
Refinement for
Future Queries

26. Checklist for Healthcare Prompt Engineering

27. 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

28. What Are Chatbots?

29. Patient Triage:
•Appropriate level of
care
Mental Health
Support:
•Immediate, cost-
effective
Patient
Education:
•Provide reliable and
continuous
information, explain
treatment options, or
clarify post-operative
care instructions.
Remote
Monitoring:
•Ensure medication
adherence, and alert
clinicians about
anomalies.
Clinical Decision
Support:
•Data-driven insights
to support clinical
decisions.
Confidentiality and
Compliance:
Ensure that all interactions are
secure and compliant with
healthcare regulations.

31. Overcoming Bias
• Anglocentrism
• Contextual Understanding
• Translation Limitations
• Data Imbalance

32. Relevance to healthcare education
• Adapts to individual student needs
Personalized
Learning:
• Creating diverse and engaging educational materials.
Content Creation:
• Interactive learning experiences (Chatbot)
Student Engagement:
• Provides real-time assessment and feedback .
Assessment and
Feedback:
• content accessible to diverse learners
Accessibility:
• Facilitates collaboration among students and educators,
bridging geographical and language barriers.
Collaboration and
Communication:

33. Personalized Learning
• Tailors educational content
Adaptive Content Delivery:
• Provides instant feedback and real-time assistance
Real-Time Feedback and
Support:
• Engages with interactive dialogues and Simulates scenarios.
Interactive Learning
Environments:
• Analyses - identify strengths and weaknesses for personalized learning.
Data-Driven Insights:
• Adapts content to diverse learners & multiple languages.
Accessibility and Inclusivity:
• Facilitates collaborative learning experiences and peer interactions.
Collaboration and Peer
Interaction:
• Seamlessly integrates with Learning Management Systems (LMS)
Integration with Existing
Platforms:
• Supports lifelong learning and Assists in tracking and maintaining
professional development
Continuous Learning and Skill
Development:
• Ensures ethical guidelines and privacy regulations.
Ethical and Privacy
Considerations:
• Aligns personalized learning experiences and Ensures relevance to real-
world medical practice
Alignment with Healthcare
Objectives:

34. Criteria Inadequate (1) Developing (2) Proficient (3) Exemplary (4)
Understanding of
Generative AI Concepts
Demonstrates limited or
incorrect understanding;
fails to integrate into
pedagogy.
Understands basic
principles but
integration into teaching
is superficial.
Strong understanding
and effective integration
into teaching methods.
Expert-level grasp,
including contributions
to curricular
development and
research.
Technical Proficiency in
Generative AI Tools
Struggles to operate
basic functions; no
integration into
teaching.
Can use basic features
but lacks fluency and
instructional application.
Competently uses a
range of features,
enhancing teaching
quality.
Mastery of features,
adapts tools for
specialized instructional
needs.
Data Analysis and
Interpretation
Cannot interpret
Generative AI-generated
data for educational
purposes.
Understands basic
analytics; limited
classroom application.
Proficient in interpreting
data to inform teaching
decisions.
Expert-level analytics
skills, contributing to
research and best
practices.
Pedagogical Integration
Fails to integrate
Generative AI into
pedagogical practices.
Basic integration; limited
impact on teaching
outcomes.
Successfully integrates
Generative AI in various
teaching methods.
Innovates pedagogy
through advanced
Generative AI
integration.
Ethical Awareness and
Application
Unaware or dismissive of
ethical considerations in
using Generative AI.
Basic awareness but
lacks full compliance and
discussion in class.
Adheres to ethical
guidelines and
incorporates discussions
in teaching.
Advocates for ethical use
and contributes to
institutional or field-
wide policies.
Professional
Development
No engagement in
professional
development related to
Generative AI.
Participates in
development sessions
but lacks follow-through.
Actively seeks and
applies new learning in
Generative AI.
Leads professional
development sessions
and contributes to the
literature.
Teacher Competency
Assessment in
Generative AI
Proposed framework by AIDE
AI for Innovative Design & Education

35. Criteria Inadequate (1) Developing (2) Proficient (3) Exemplary (4)
Understanding of
Generative AI Concepts
Shows limited or
incorrect understanding;
fails in practical
application.
Grasps basic principles
but lacks depth in
coursework or research.
Strong understanding;
can articulate and apply
in academic tasks.
Expert-level
understanding; possibly
contributing to student-
led research or projects.
Technical Proficiency in
Generative AI Tools
Struggles to operate
even basic functions;
poor academic
application.
Capable with basic
features but lacks
advanced skills; limited
research application.
Competently uses a
variety of features for
academic tasks.
Mastery of features;
adapts tools for
specialized academic or
research tasks.
Data Analysis and
Interpretation
Cannot interpret
Generative AI-generated
data for coursework or
research.
Understands basic
outputs; limited
application in academic
tasks.
Proficient in interpreting
a variety of data outputs
for academic uses.
Expert-level analytics;
may contribute to
research or advanced
academic projects.
Academic Application
Fails to apply Generative
AI tools to academic
tasks.
Some application to
academic tasks but lacks
depth.
Consistently applies
Generative AI tools to
enhance academic tasks.
Advanced application,
significantly contributing
to research or
coursework.
Ethical Awareness and
Application
Unaware or ignores
ethical considerations of
using Generative AI.
Basic awareness but
lacks in-depth
understanding and
application.
Adheres to ethical
guidelines and discusses
implications in academic
work.
Advocates for ethical
use; possibly
contributing to student
or institutional policies.
Collaborative Skills
Struggles to collaborate
effectively in Generative
AI tasks.
Can collaborate but
contributes minimally to
the Generative AI
component.
Actively contributes to
collaborative efforts,
enhancing team
performance.
Leads collaborative
projects, optimizing
team performance in
Generative AI
applications.
Student Competency
Assessment in
Generative AI
Proposed framework by AIDE
AI for Innovative Design & Education

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

38. 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

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

43. https://creator.nightcafe.studio/

45. Educational videos
• Be concise
• Mobile-compatible
• Optimized for social
media
• Enhance blended
learning Average view time of 1.72 min
(103 Seconds)

46. AI for Video Production
Draft
Learning
Outcomes
LO to Prompt
ChatGPT for
video script
Import/edit
script to AI
Video
Generator
Add
personalised
media
Choose
Voiceover
type
Produce
Review and
Upload

47. 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

48. AI generated Instructional Video

49. 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

50. What are the
antibiotics for
leprosy
treatment

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

52. “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

53. 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)

56. Elicit.org

57. Using SCISPACE
Analyse your documents with Generative AI

58. Add SciSpace Copilot to your browser
AI research assistant that explains the text, math, and tables in
scientific literature like research papers, technical blog posts, or
reports. You can also ask follow-up questions, and it will give
you instant answers.

59. Create Account and Login

60. Choose PDF file to upload

61. Add to your
collection of
choice and
click DONE

62. Interrogate
the paper
as per
needs

63. Input your research topic

64. Outcome for
Topic Inserted
in search box

65. Click
Summarized
Abstract to
ADD to
COLUMN in
table

66. CLICK on a
paper to
see
summaries
and
Interrogate
as per
needs

67. Typeset.io

68. The Art and Science of Qualitative Research
https://tinyurl.com/QUALIRE
Introduction to research in healthcare
https://tinyurl.com/HCARERE
AICHAT BT FOR Research in healthcare
https://tinyurl.com/HCAREREBOT
AI-Powered Academic Writing Write Your Research Paper in a Day
https://tinyurl.com/AIAWRITE
AI CHAT BOT for AI_POWERED ACADEMIC WRITING
https://tinyurl.com/AIAWRITEBOT

70. Upcoming
• Ministry of Science, Technology
an Innovation, Malaysia
• UNESCO, Thailand
• iCERI, Spain
• Sengkang General Hospital,
Singapore