Presented to the University of Virginia Medical Education Week on Feb 26, 2024.

1. AI IN MEDICAL
EDUCATION
A META VIEW TO START A
CONVERSATION
Philip E. Bourne PhD, FACMI, February 26, 2024. https://www.slideshare.net/pebourne

2. – I have no formal medical school
training
– I was the first Chief Data Officer
of NIH
– I have taught many pharmacy
students
– I tend to see everything through
the lens of data
– AI is only part of the story
– I complained that medical school
curricula were not in keeping with
DISCLAIMER/BIA
S

3. THE (NON-MEDICAL) STUDENTS HAVE
SPOKEN
– Its here, it’s a tool
– A tool like never before
– How to relate to the honor code
– Don’t get caught up in the hype
– Wish professors knew more
– Want to know more about the
implications
Soon to be posted

4. HOW DID WE GET HERE?
– AI consumes data of all types:
90% of the worlds data was
generated in the last 2 years –
Medical data is a mess – Kudos
to GW Bush
– Improved computer technology –
UVA was behind but is catching
up
– Breakthroughs in algorithms and
hence software providing
persistent models – This has got
everyone's attention
A zetabyte is 1012 gigabytes

5. CONSIDER OUR FAVORITE
EXAMPLE -
– ChatGPT is one of many forms of AI – A Large Language
Model
– ”G” generative – ability to generate language,
images, video, code …
– “P” pre-trained – unsupervised learning on vast
amounts of content
– The training is done by neural networks that mimic
the brain – learning by adjusting weights of each
“neuron/node). Training stops when the right result is
achieved. That network is then a model that can
produce {mostly} the right answer from data it has
never seen before
– ”T” Transformers allow for parallel computation and
treats text etc. as tokens
ChatGPT

6. – Diagnostic and image analysis
– Predictive analytics
– Personalized medicine
– Drug discovery
– Robot assisted surgery
– Virtual health assistants
– Clinical trials research
– Wearables
– Healthcare operations
– Mental health applications
THE NOW -
EXAMPLES

7. WHERE ARE WE HEADED?
The current deep neural networks are
equivalent to a rice grained size of the
cerebral cortex and we are yet to explore
most aspects of brain morphology
Terry Sejnowski https://www.pnas.org/doi/full/10.1073/pnas.1907373117

8. What Does This Mean in
Practical Terms with AI

9. – AR changes the student-
student; student-patient
dynamic
– LLMs provide a rich training
ground
– Student-mentor
relationships will be
different, but remain
important.
PEDAGOGY
Images by DALL-E

10. RESEARCH
A Biomedical Researcher
The Holy Grail of Molecular Biology
Food production
Energy production
Drugs …
– Achieved by DeepMind (a Google
spin off) not academia
– 30 interdisciplinary scientists
working together not competing
– Compute power beyond a
university

11. THREATS
OPPORTUNITIES
Creating a new health care industry

12. How Disruptive Will It Be?

13. THE 6 D’S (PETER DIAMANDIS)
Digitization
Disruption
Demonetization
Dematerialization
Democratization
Time
Volume,
Velocity,
Variety
Digital media becomes bona fide
form of communication
Deception

14. KODAK – A 6D’S CASE STUDY
Digital media becomes bona fide
form of communication

15. WILL HISTORY REPEATS ITSELF?
MEDICINE
Digitization
Deception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volume,
Velocity,
Variety
AI impact minimal
Models reach human capacity
Augmented reality, sensors
Quantum computing
Digital media becomes bona fide
form of communication
Learning modalities change
Knowledge workers must adapt
job market shifts
Robotics
Research practice changes

16. THANK YOU
Philip E. Bourne February 21, 2024, Leadership Meeeting

17. TWO DIFFERENT FUTURES ACCORDING
TO DALL-E