AI, Superintelligence, and the Future of Clinical Reasoning

 AI, Superintelligence, and the Future of Clinical Reasoning

By John E. Delzell Jr., MD, MSPH, MBA, FAAFP

In medical education, we often talk about transformation. Competency-based education, interprofessional learning, simulation, and evidence-based practice have all changed how we prepare the next generation of physicians. But the greatest transformation still on the horizon—and it’s being driven by artificial intelligence (AI).

Over the past few years, AI has moved rapidly from theoretical potential to practical application. Large language models (LLMs) like GPT-4 have already demonstrated the ability to pass the USMLE, summarize research articles, and assist in clinical decision-making. As stated by King and Nori (1), “reducing medicine to one-shot answers on multiple-choice questions, such (USMLE) benchmarks overstate the apparent competence of AI systems and obscure their limitations.” Even so it is clear that we are entering a new era where AI may not just assist doctors—it will outperform them in specific domains. 

What does this mean for medical education? To answer that, we have to look at where the technology is headed and how it intersects with human learning, reasoning, and clinical judgment.

The Promise and Peril of Medical Superintelligence

In June 2025, Microsoft and OpenAI (1)  released a visionary statement on the trajectory toward “medical superintelligence”—a form of AI that not only surpasses human performance on standardized medical benchmarks but demonstrates generalizable clinical reasoning across specialties. Their goal is to build a system that operates with “generalist-like” breadth and “specialist-level” depth, grounded in real-time reasoning and safety. This is not science fiction.

In a recent preprint (2), OpenAI researchers presented GPT-4-MED, an experimental model fine-tuned specifically on clinical data. The team created a set of 304 digital clinical cases that came from the New England Journal of Medicine clinicopathological conference (NEJM-CPC) cases. (2) The cases are stepwise diagnostic encounters where physicians can iteratively ask questions and order tests. As new information becomes available, the physician updates their reasoning, eventually narrowing towards their best final diagnosis. The final diagnosis can then be compared to the “correct” diagnosis which was published in the journal. When I was a student and resident, I loved reading these. I almost never got the correct diagnosis, but I learned a lot from the process. 

In structured evaluations, the new AI model outperformed existing models on dozens of medical tasks, from radiology interpretation to treatment planning. And importantly, when tested against human physician performance on the NEJM cases, it demonstrated reasoning that mimics human diagnostic thinking: considering differential diagnoses, weighing risks and benefits, and accounting for uncertainty.

This level of performance hints at the possibility of AI not only augmenting care but becoming a form of clinical intelligence in its own right. In short, we are no longer talking about tools. We are talking about future colleagues.

Cognitive Load, Expertise, and What Makes a Good Doctor

If AI systems can increasingly perform tasks once limited to trained physicians, what remains uniquely human in the physician role? One answer lies in how we process complexity.

A 2022 paper by Tschandl et al (3) explored how AI and human physicians interact in diagnostic decision-making. Their findings are fascinating: when AI is presented as a peer or assistant, it improves physician accuracy; but when it is given too much credibility (ie: treated as an oracle), physicians defer too quickly, losing the benefits of independent judgment. In essence, the relationship between humans and AI is dynamic—shaped by trust, communication, and cognitive calibration.

This has major implications for education. Medical students and residents must not only learn the traditional content of medicine; they must learn how to work with AI systems—to question, validate, and contextualize recommendations. That means we must teach not just clinical knowledge but metacognition: the ability to understand how we think, and how machines think differently.

And we must recognize that human expertise is not obsolete. As Microsoft notes in its roadmap to superintelligence, there are still many domains where AI falls short—especially in interpreting nuance, assessing values, and navigating ethical complexity.(1) These are precisely the areas where medical educators must continue to lead.

A New Role for the Medical Educator

So how should medical educators respond?

First, we must integrate AI literacy into the curriculum. Just as we teach evidence-based medicine, we now need to teach “AI-based medicine.” Students should understand how these models are trained, what their limitations are, and how to critically appraise their output. This isn’t just informatics—it’s foundational clinical reasoning in the 21st century.

Second, we need to reimagine assessment. Traditional exams measure knowledge recall and algorithmic thinking. But AI can now generate textbook answers on command. Instead, we should assess higher-order skills: contextual judgment, empathy, shared decision-making, and the ability to synthesize information across disciplines. We are not trying to train machines—we are trying to train humans to be the kind of doctors AI can’t be.

Third, we must prepare for a changing scope of practice. As AI takes on more diagnostic and administrative tasks, physicians may find themselves able to focus more on the human aspects of care—narrative, empathy, ethics, and meaning. This is not a diminishment of the physician’s role. It is a refinement. We are moving from being knowledge providers to wisdom facilitators.

The Human-AI Team

One of the most powerful concepts in Microsoft’s vision is the idea of the human-AI team. This is not about replacing doctors with algorithms. It’s about creating a partnership where each party brings unique strengths. AI can process terabytes of data, recognize subtle patterns, and recall every guideline ever published. Humans can listen, connect, and weigh values in the face of uncertainty.

As educators, we must train our learners to be effective members of this team. That means not just accepting AI, but shaping it—participating in its development, informing its design, and advocating for systems that reflect the realities of clinical care. This will not be easy. There will be challenges around bias, privacy, overreliance, and professional identity. But the alternative—ignoring these changes or resisting them—is no longer tenable.

Looking Ahead

Medical education is entering a new frontier. In the coming years, we will need to train learners who are not only competent clinicians, but also agile learners, critical thinkers, and collaborative partners with AI.

This is not the end of the physician. It is the beginning of a new kind of doctor—one who uses technology not as a crutch, but as an amplifier of what makes us human.

And that, to me, is the real promise of medical superintelligence: not that it will replace us, but that it will help us become even better at what we are meant to do—care for people in all their complexity.

References

(1) King D, Nori H. “The path to medical superintelligence”  Microsoft AI. Accessed 7/8/25. Retrieved from https://microsoft.ai/new/the-path-to-medical-superintelligence 

(2) Nori H, Daswani M, Kelly C, Lundberg S, et al. Sequential Diagnosis with Language Models. Cornell University arXiv.  Accessed 7/7/25. Retrieved from https://arxiv.org/abs/2506.22405 

(3) Tschandl P, Rinner C, Apalla Z. et al. Human–computer collaboration for skin cancer recognition. Nat Med 2020; 26: 1229–1234. https://doi.org/10.1038/s41591-020-0942-0

Running the Distance: Joy, Risk, and Why I Keep Lacing Up

John E. Delzell Jr., MD, MSPH, MBA, FAAFP

I still remember the first time I crossed the finish line of a marathon.

It was hot. We were in Orlando (the Disney Marathon). My legs were toast. The crowds were cheering. I definitely cried in those final few meters. Finishing 26.2 miles doesn’t just test your body. It tests your commitment, your mind, your pain threshold, and sometimes your relationship with your toenails.

I’ve run a lot of races since then. Some were fast. Some were slow. Some were surprisingly fun. Others… let’s just say I was glad they ended. But each one taught me something—not just about pacing or hydration, but about myself. About resilience. About joy. About being present in motion.

So, when I recently came across two very different—but equally important—articles on running, I felt compelled to dig a little deeper.

Why We Run (And Why I Still Do)

Let’s start with the why. In 2021, Hugo Vieira Pereira and colleagues (1) published a systematic review in Frontiers in Psychology that asked a simple but profound question: What drives people to run for fun?

Not surprisingly, it’s not about weight loss or physical health—although those show up plenty. They found that psychological and behavioral factors play just as large a role, especially for recreational runners. Things like stress relief, mood elevation, a sense of achievement, or just the pure enjoyment of the run itself. I do enjoy the bling, nothing like posting that picture of your finisher medal on your favorite social media site, but there is much more to the joy of running than a medal. Interestingly, runners with more experience tend to internalize the joy—shifting away from extrinsic motivations (like medals or fitness) toward more intrinsic ones (like emotional well-being or identity).

I get that. Running has long been a reset button. It’s where I process tough days, pray, think, unwind. It’s where I go when I need space, and oddly enough, also where I go when I need community. The running community is incredibly supportive. Long runs with friends have a way of cutting through small talk. You learn a lot about someone at mile 16.

The Pereira review also highlights how consistent runners tend to have high self-regulation skills—planning, goal-setting, time management, and the ability to push through discomfort. That sounds right. You don’t finish a marathon on motivation alone. You finish it because you ran all the invisible miles in the dark before sunrise, when no one was cheering.

The Hidden Risk No One Talks About on Race Day

But running isn’t all runner’s highs and finish-line photos. Every time I pin on a bib number, especially at marathons or halfs, I know I’m also assuming a small—but real—risk. And that brings us to the second article.  Published in 2025 in JAMA, the study by Kim et al (2) tackled the sobering topic of cardiac arrest during long-distance running races. The researchers reviewed over a decade’s worth of data and identified several critical insights:

- Cardiac arrest during organized races is really rare—occurring in about 1 per 100,000 participants—but considering the number of race participants (>23M) not negligible.
- Most cases occurred during marathons (not shorter distances), and more often near the end of the race.
- Interestingly, the incidence of cardiac arrest is stable (compared to 2000-2009) but there has been a significant decline in mortality
- Bystander CPR and the presence of automated external defibrillators (AEDs) significantly improved survival.

As a physician, I’ve always known running carries cardiovascular risk, especially if there’s underlying heart disease, electrolyte imbalances, or unrecognized genetic issues like hypertrophic cardiomyopathy. But reading this paper hit me a little differently—because it’s about my people. My tribe. Ordinary folks pushing themselves to extraordinary limits. As a runner, it reminded me that health screening and preparation matter—even when you’re “fit.” It’s easy to assume that crossing the start line means you're healthy enough. But racing is different from running. The adrenaline, the intensity, the heat, the dehydration—all of it combines into a stress test with real consequences.

Running Smarter, Running Longer

So how do I reconcile the joy of running with the risk it carries?  Honestly, it’s the same way I’ve practiced medicine for 30 years: with a clear-eyed look at the data and a respect for human experience.

First, I take precautions seriously. Regular checkups. Listening to my body. Hydration. Electrolytes. And yes, even slowing down when needed. No PR is worth collapsing for.

Second, I keep running for the same reasons that I started running. I’m not chasing record times anymore. I’m chasing clarity. Fellowship. Flow. Those long runs that leave your muscles sore but your spirit full.

And third, I encourage others, especially new runners, to train smart and listen to their body. Get checked out by your primary care doctor if you are over 40 and new to endurance sports. Don’t ignore chest discomfort, dizziness, or feeling “off” on race day. Carry ID. Know where the aid stations and AEDs are. Be the person who knows CPR.

The truth is, running can be one of the most powerful mental and physical health interventions we have—when done right.

My Finish Lines and What They Taught Me

Each marathon I’ve run has carried its own story. The one where it rained the whole time. The one where I cramped at mile 18. The one I ran with my best friend from high school cheering me at the finish line. Each race reminded me that finishing isn’t about being fast—it’s about being faithful to the training, the effort, the journey.

I’ve been lucky. I’ve stayed healthy, mostly. I’ve never DNF’d. But I’ve seen people collapse. In our first half marathon, a man collapsed and got bystander chest compressions on the course (he lived!). I’ve slowed down to walk someone to the medical tent. And I’ve always been thankful to cross the line—upright, tired, and deeply grateful.

Final Thoughts

Whether you’re finishing first or finishing last, there’s something sacred about committing your body and mind to something hard and seeing it through. Something human.  Running, like life, holds both joy and risk. We run to feel alive, to cope, to connect, to challenge ourselves. And while the road can be unpredictable—especially over 26.2 miles—it’s also where I’ve found some of my clearest moments.

So yes, I’ll keep lacing up. I’ll keep being smart. I’ll keep showing up.

The finish line may only last a few seconds, but the lessons from the road last a lifetime.

References

(1)  Pereira HV, et al. Systematic Review of Psychological and Behavioral Correlates of Recreational Running. Front. Psychol., 06 May 2021; Volume 12  https://doi.org/10.3389/fpsyg.2021.624783  

(2)  Kim JH, Rim AJ, Miller JT, et al. Cardiac Arrest During Long-Distance Running Races. JAMA. 2025;333(19):1699–1707. doi:10.1001/jama.2025.3026