What Makes a Great Doctor: How AI Is Redefining Excellence

I recently came across the idea that AI would turn software engineers from bricklayers into architects. But we look for different abilities in a great bricklayer and a great architect. If someone moves from one to the other, we normally say they changed professions. In software, we might keep the title even as the work underneath it changes.

Try a thought experiment. Take a profession and write down the abilities that would have made someone exceptional at it five years ago. Then ask what happens to each as AI becomes more capable. Some might matter less, others more. New abilities enter the picture. Would we still recognize the same people as exceptional?

Writing reliable code quickly has long distinguished strong developers. If AI narrows differences in that ability, understanding the problem, choosing a solution, and evaluating the result may account for more of what makes someone exceptional.

My sister is an architect, and I remember her studying during the transition from physical architectural models to digital tools. Model-making can help develop an idea, but skill at constructing the model also affects how well someone can express it. Changing the tools changes how much that particular ability matters. It could reveal design strengths we previously underestimated.

The Impact of AI

That possibility interests me most in medicine. Could AI change which people become exceptional physicians? And would we recognize them using the same signals we looked for in medical school?

I think of students who could absorb enormous amounts of information and produce the right answer under pressure. But being an excellent student, becoming a prestigious physician, and providing exceptional care are different accomplishments. We shouldn’t assume the same abilities explain all three.

For this experiment, I would examine four: retaining medical knowledge, reasoning through problems, obtaining reliable information from patients, and turning decisions into effective care. The balance differs by specialty, but this gives us something more specific to examine than “good judgment.”

Suppose AI makes relevant medical information much easier to retrieve and apply. Rapid recall becomes less distinguishing while understanding remains essential: a physician still needs to recognize a bad recommendation. An ability can remain necessary without conferring the same advantage over one’s peers.

Reasoning belongs inside the experiment too. It is tempting to say machines will supply facts while physicians supply judgment. But what happens if tools become increasingly capable of connecting findings and comparing treatments? Deep expertise might help a physician use them better. We cannot assume judgment itself will remain unchanged.

Now imagine a physician who was never the fastest at recalling information in class, but is unusually good at getting patients to explain what is happening. They discover that a patient stopped taking a medication that everyone else assumed was still being taken. If tools narrow the gap in recall, this physician’s strength could account for more of what distinguishes them. It was always valuable; the balance has changed.

The same applies to making care happen. Someone must perform the procedure, explain the choices, and work out whether the patient can follow the plan. A treatment accomplishes little if the patient cannot obtain it. Producing a recommendation more easily makes these remaining difficulties harder to ignore.

The Organization Decision

Working with AI also introduces situations that require practice: deciding what to delegate, detecting unsupported conclusions, and resolving disagreements with the system. Physicians need to notice when the tool helps them think and when it encourages them to stop thinking too soon.

For someone already practicing, this is a useful way to consider what to develop next. Which strengths should I deepen? What can I now accomplish despite a previous limitation? What should I learn that my training never required?

Then take the experiment up a level. If the abilities required to deliver good care change, why would healthcare remain organized in the same way?

We organize care around professions that each bring a particular combination of abilities. If a nurse supported by AI can safely perform an activity that previously required a physician, the implications extend beyond making either person more productive. It changes who can provide that care. Repeat that across enough activities, and the division of work between professions starts to look different.

My hypothesis is that some of healthcare’s largest changes will come from reorganizing care around this new distribution of abilities. Cheaper clinical interpretation does not automatically provide access to examinations, procedures, or treatment. Understanding what remains difficult tells us where the next opportunity may be.

A profession carries assumptions about what someone knows, what they can do, and how we identify the best people. Those assumptions shape education, hiring, and entire organizations. They deserve another look when the work changes.

We may keep calling people physicians, engineers, and architects. But if we keep looking for excellence in exactly the same places, we could miss both the people and the organizations capable of delivering it next.