06/26/2023    Wenjay Sung, DPM

Artificial intelligence and the Augmented Practice of Medicine

With the release of ChatGPT by OpenAI, the general
public has begun to understand the implications
and impact of a future with AI at their
fingertips. What was seen as science fiction is
now becoming closer to the present, but there is a
rising fear of a coming world where human centric
work becomes obsolete.

Medicine maybe the highest pillar to climb for
technology to advance beyond need of human
intervention. However, health care professionals
should already know lower tier processing systems
have already been a part of the healthcare
experience for decades. Although “AI” is thrown a
lot as the new buzzworthy term to be designated as
any system of data processing, there are
distinctions and levels of processing that must be
understood to fully gage the timeline of impact to
the medical profession.

Healthcare providers and their professional
societies generally have recommended treatment
plans for certain diseases, and these could be
extrapolated as algorithms. It is important to
remember that AI and algorithms are related
concepts, but they have distinct differences. An
algorithm is a step-by-step set of instructions or
rules to solve a specific problem or perform a
task. Algorithms have been used for centuries in
various fields and are essentially predefined
procedures or calculations. In healthcare, the
more complex the disease, the more delicate and
deliberate the algorithm. As the complexities of
certain diseases and their treatments or
prevention are better understood, algorithms
simply help with the tasks of triage or care
assignments. In other words, algorithms can be
reduced to limited directional processes that
provide routing information much like a map.
However, they obviously have limitations, lacking
the ability to extrapolate and create unique
ideas.

Improvement upon simple algorithm processes began
with machine learning. In 19491 with the author
Donald Hebb described the communication process
between neurons while touting theories of
artificially created neural networks. This
expanded in the 1970’s and 80’s2 as computers were
able to increase the speed of their processing.
For decades now, machine learning, big data, and
neural network algorithmic processes have been
touted by healthcare futurists as assessable
predictive engines for prophylactic preparedness
against disease outbreaks.3 However, as ChatGPT
has prompted, diagnostics, increased productive,
and individualized treatment plans may be in the
first wave of benefits for healthcare providers4.
It is this promise of AI that potentially enables
machines to learn, reason, and make decisions
similar (if not better) to humans.

While algorithms are deterministic and follow a
predefined set of rules, AI algorithms can adapt
and improve their performance based on data and
experience. AI algorithms can learn from patterns,
make predictions, and generalize knowledge to new
situations. They can handle complex and
unstructured data, learn from large datasets, and
discover insights that may not be apparent through
traditional algorithms. Algorithms are specific
sets of instructions to solve a problem, while AI
involves the broader concept of machines mimicking
human intelligence and learning from data to
perform complex tasks.

However, this requires huge amounts of data,
powerful, expensive computers to process this
information, and time to trial and determine if
the results are significantly better than
previous. For example, the average cost for four
years of medical school in the U.S is nearly
$250,0005. Residency training can be 3-7 years
with or without a few extra years for fellowship
training. In total these costs to train doctors
to independently join the work force may be close
to one million dollars. The average cost for
OpenAI to run ChatGPT version 4 is $700,000 a
day6. This does not include the costs of creation
of all other generations of ChatGPT. Although
many corporate companies tout AI software
solutions tailored to your needs through a lease
of an AI program, these leases still cost about
$500,000 a year7. Although AI may exist in the
healthcare space, it may not be as cost productive
as training new healthcare providers.

It's important to note that while AI in healthcare
may offer numerous benefits, it should complement
the expertise and judgment of healthcare
providers, rather than replace them. Ethical
considerations, data privacy, and ongoing research
are crucial in harnessing AI's potential in
healthcare effectively. Currently, there are
numerous examples of healthcare providers
utilizing AI in practice today.

In the field of radiology, computer-aided
detection was first cited in 1992 to detect micro-
calcifications in mammograms8. This groundbreaking
study lead to radiologists and healthcare
providers to imagine how AI could help reduce the
work load burden, providing accurate diagnoses.
AI-based systems such as workflow automation was
adopted by most hospitals and helped clinicians
with real-time decision support by analyzing
patient data, medical records, and clinical
guidelines. Automating operational tasks, such as
the evaluation of imaging quality, patient
coordination, and improvement of disease reporting
have been incorporated in several hospital systems
with the assistance of AI9. These systems have
offered treatment recommendations, alert
physicians to potential drug interactions or
adverse events, and aid in identifying the most
effective treatment options. This has become vital
after the post-covid staffing issues.

The increasing usage of virtual chatbots are
another example of adaptive medicine with AI due
to staffing issues post-COVID. These AI-powered
virtual assistants and chatbots have been used to
provide basic medical information, answer patient
queries, and schedule appointments. Also, as more
and more patients turn toward the internet for
healthcare information, healthcare seekers are
becoming more comfortable with asking virtual
chatbots for personalized health recommendations
based on symptoms10. Although tests comparing
answers by virtual chatbots like ChatGPT versus
actual physicians have shown mixed results, some
patients preferred the chatbots due to higher
empathy in the answers10.

Wearable devices that monitor various health
parameters, such as heart rate, blood pressure,
and sleep patterns have also become a field where
AI can improve healthcare. These devices can track
changes, provide health insights, and alert
healthcare providers when intervention is
required, and many companies have invested to be
the go-to wearable device. The reason is because
AI for individualized care requires lots and lots
of data. Wearable devices that are worn hours
throughout the day and night can track changes and
monitor vital signs constantly, thus improving AI
recommendations.11

These examples demonstrate how AI is already being
used across different aspects of healthcare to
improve diagnosis, treatment, monitoring, and
patient engagement. The adoption of AI
technologies continues to evolve, with healthcare
providers exploring new applications and expanding
the integration of AI into their practices. More
importantly, AI is still decades away from
replacing actual human doctors, if ever, but
increasing important for healthcare providers to
use AI in their perspective field. Much like a
revolutionary medical treatment, AI is becoming a
critical tool to augment the practice of medicine
and practicing physicians should not avoid
engaging with AI.

Wenjay Sung, DPM, Arcadia, CA

Dr Sung is an early seed investor in Ahura AI and
an actively practicing podiatrist in Arcadia, CA.

References
1. Hebb, D. O. The organization of behavior: A
neuropsychological theory. New York: Routledge,
Taylor & Francis Group, 1949.
2. Foote, Keith D. “A Brief History of Machine
Learning.” DATAVERSITY, December 3, 2021.
https://www.dataversity.net/a-brief-history-of-
machine-learning/.
3. Mayer-Schönberger, V., & Cukier, K. (2013). Big
Data: A revolution that will transform how we
live, work and think.
4.ChatGPT. “The Implications of on Podiatry
Practice.” Podiatry Management Online, 2023.
https://podiatrym.com/Current_Issue2.cfm?id=3069.
5. Hanson, Melanie. “Average Cost of Medical
School” EducationData.org, May 18, 2023,
https://educationdata.org/average-cost-of-medical-
school
6. Chowdhury, Hasan. “Chatgpt Cost a Fortune to
Make with OpenAI’s Losses Growing to $540 Million
Last Year, Report Says.” Yahoo! Finance, May 5,
2023. https://finance.yahoo.com/news/chatgpt-cost-
bomb-openais-losses-
125101043.html#:':text=Last%20month%2C%20Dylan%20P
atel%2C%20chief,costs%20involved%20with%20computin
g%20power.
7. Palokangas, Elmeri. “How Much Does Ai Cost?
What to Consider.” Scribe, May 23, 2023.
https://scribehow.com/library/cost-of-
ai#:':text=of%20AI%20solutions!-,What%27s%20the%20
average%20cost%20for%20AI%20solutions%3F,to%20as%2
0little%20as%20%240
8.Driver CN, Bowles BS, Bartholmai BJ, et al.
Artificial Intelligence in Radiology: A call for
Thoughtful Application. Clin Transl Sci 2020; 13:
216–218
9.Cowen, Laura. “How Artificial Intelligence Is
Driving Changes in Radiology.” Inside Precision
Medicine, February 14, 2023.
https://www.insideprecisionmedicine.com/artificial
-intelligence/how-artificial-intelligence-is-
driving-changes-in-radiology/
10.Staff, CBS Baltimore. “Why Are Patients Turning
to Artificial Intelligence Chatbots for Medical
Advice?” CBS News, May 11, 2023.
https://www.cbsnews.com/baltimore/news/patients-
chatbots-johns-hopkins-medical-artificial-
intelligence/
11.Rosen, Howard. “Council Post: How Generative AI
Can Improve Personalized Healthcare with Wearable
Devices.” Forbes, April 17, 2023.
https://www.forbes.com/sites/forbesbusinesscouncil
/2023/04/14/how-generative-ai-can-improve-
personalized-healthcare-with-wearable-devices/?
sh=51e900f1a3c9

Other messages in this thread:

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06/27/2023    Michael L. Brody DPM

Artificial intelligence and the Augmented Practice of Medicine (Wenjay Sung, DPM)

The Journal of the American Bar Association
recently published an article titled What
cybersecurity threats do generative AI chatbots
like ChatGPT pose to lawyers?

Software tools such as ChatGPT are 'not ready for
prime time' when it comes to Healthcare due to the
reasons that are outlined in the article. I am
optimistic about the future of IA as a tool for
the future of medicine. Note: I use the term IA -
Intelligence Augmented as opposed to artificial
intelligence. These are tools to assist us in
clinical decision-making, not tools to make
decisions for us.

When it comes to tools such as ChatGPT they are
based upon huge databases. What is in the
databases? Is the information that is being used
by the tool accurate or has inaccurate or
erroneous information made its way into the
database? Let’s assume that the database is a
database of all clinical notes for all physicians
in the country. That would be a vast treasure
trove of information to power the tool. But I ask
you this question. How many times have you seen
clinical notes generated by an EHR system that is
full of contradictory and boilerplate language
that is either inaccurate or even fabricated by
the EHR program? Is this information in the
database the tool is using to assist me in
clinical decision-making? When the security is in
place to ensure that use of a clinical decision
support tool will not compromise the privacy or
security of my patient information I will begin to
use these tools.

Once I do use these tools, I will use the
information to inform me as part of my clinical
decision process. Let’s stay informed as to what
is happening and wait until the tools do not pose
a risk to our patients.

Many EHR vendors are looking at IA tools to
enhance their products. Ask them about the
database that their tool is based on and if the
information you put into the tool (including
potential patient information) gets stored in that
database and if it is possible that if you use the
tool the PHI (protected health information) of
your patients may be seen by other users of the
tool.

Michael L. Brody DPM, Commack, NY