Artificial intelligence and the Augmented Practice of Medicine

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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 1941 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, June 28, 2023

Dr Sung is an early seed investor in Ahura AI, partner at Valadares USA, and is actively practicing as a podiatrist in Arcadia, CA.


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,

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%20Patel%2C%20chief,costs%20involved%20with%20computing%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%20average%20cost%20for%20AI%20solutions%3F,to%20as%20little%20as%20%240

  1. 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