Opinion | Being a Doctor Will Never Be the Same After A.I.

I have a confession: A.I. is making me a better doctor. And I worry that it’s making doctors-in-training worse.

Amid our recent doom cycle about whether A.I. will lead to extreme societal harms, a parallel anxiety has taken hold in medicine about whether A.I. will one day replace physicians. In the context of that panic, I feel a little sheepish admitting that I already use the technology in my practice all the time — especially since I harbor anxieties about what its availability means for my students and residents, and therefore for the profession at large.

This summer, I used an A.I. tool — a platform called OpenEvidence, accessible only to health care providers — to help me choose antibiotics for one patient and to interpret unusual bloodwork for another. A.I. reminded me to consider migraines when a woman presented with dizziness. I chatted with the large language model about treatments for an older man with lung cancer. I have also consulted it about a rash on my father’s back, my son’s fever, a friend’s arm pain and my own difficulty sleeping.

I practice primary care, where every malady is my business. Almost every month, I’m confronted with a complaint or syndrome that I don’t recognize. I’m confident in my mastery over what doctors do — history-taking, reasoning, communicating. I’m less confident about my continued mastery of an ever-evolving universe of facts. Now, the chatbot in my pocket reassures me that I will always know enough, or have access to the cloud-based intelligence that generally does.

All of my students and residents also use OpenEvidence, however, and I worry about the consequences of introducing such a powerful decision aid so early in their careers. Not only have they memorized less than I’d like, but they can seem almost passive in their relationship to their machines.

It’s impractical and counterproductive to train physicians in 2026 without exposure to A.I. Doctoring — as opposed to writing, my other work — is not a creative act that needs to be protected from technological annexation. It’s a service that ought to embrace any resource that improves patient care. Training doctors in the A.I. age, then, means training them to think critically with a machine in the loop, for a speculative future of medicine that we can only glimpse.

I started medical school 20 years ago. Every day since, I have looked something up, skimmed a journal article or talked through a case with a colleague — medicine is a phone-a-friend profession. As a student, I walked around the hospital with “Pocket Medicine,” a standard-issue guide, tucked into my white coat pocket, and I consulted it all day long. I had to understand exactly what I was looking for when I flipped through its pages, and it could not tell me what to do with whatever knowledge I found there.

I haven’t carried “Pocket Medicine” for years. Instead, I am never without my phone. For years, I primarily took my questions to the widely used website UpToDate.com, an evidence-based medical encyclopedia. But now, several times a day, I use OpenEvidence.

OpenEvidence is trained exclusively on peer-reviewed evidence and guidelines, and includes citations for all its claims. Not every insight found there is useful or even accurate, because much of the medical literature it refers to falls short on both those counts. To use the app well, you must stay skeptical as you scroll. It does, however, allow me to trace a statement to a source so I can evaluate it for myself. OpenEvidence reports that over half of American doctors regularly use the app, and it recently announced a plan to expand free access to doctors in developing nations.

OpenEvidence and I can have an iterative conversation grounded in a case’s peculiarities, a difference in kind from when I used to look up general information and try to apply it to an individual situation. The app lets me input my patient’s labs, or upload an imaging report photo, and then offers suggestions about what it sees. Not infrequently, it suggests something I did not consider.

I’m surprised by how much I learn from, and enjoy, these interactions. More important, my patient care has improved as conversations with the agent push my thinking beyond its usual limits.

For the residents I supervise, however, the app’s speed and anticipatory helpfulness are a hindrance as much as an enhancement. When I skim a chatbot’s response to a question about back pain, I’m intuitively testing its suggestions against my hard-won mental model for how to approach back pain. Could I have developed that if I’d had a robot leading my thinking all along?

I asked Adam Rodman, a physician and A.I. researcher at Harvard Medical School, how to think about these risks. He said he shared my concern that trainees aren’t benefiting from “productive struggles” as much as their predecessors. “In experienced hands, decision support might cause a little de-skilling, but a little de-skilling probably doesn’t matter much to you,” Dr. Rodman told me. “But if you do that to somebody who has much less skill, the cognitive offloading can cause never-skilling.”

I don’t think it’s critical that my residents memorize every diagnostic protocol, just as it’s no longer critical that I recall my friends’ phone numbers. But I do want them to learn to hear problems the way a doctor is supposed to, as stories that contain clues that set off a cascade of cognitive associations. And I want them to develop the cardinal instincts that amount to good judgment in medicine: the ability to distinguish sick from not sick and to prioritize competing patient concerns, as well as the tact to effectively seek information beyond what a patient voluntarily reveals.

A.I. is only the latest tool to provoke a reckoning around which skills are truly essential to doctoring. As a student, I struggled valiantly to identify heart murmurs in my patients, even though I had access to imaging techniques that made my stethoscope somewhat redundant. Similarly, students today need opportunities to build cognitive muscles through study, even if they will seldom rely on their own memories over their machines.

Dr. Rodman shared ideas for how to help learners experience worthwhile friction despite the availability of A.I. — proposals like A.I.-free clinical reasoning sessions and A.I. literacy training. We don’t yet know, however, what caring for patients will even look like once artificial intelligence is fully integrated into medicine, nor what its capabilities will be.

Current doctors-to-be may be the most adversely affected generation when it comes to A.I. They’re training before we know enough about how to teach them well in this new paradigm. I want their patients to benefit, as I think mine are, from doctors supported by these tools. But I also want them to develop a talent for reasoning their way through complex problems full of distractions and ambiguities, and rich with emotional stakes.

The hardest parts of doctoring will stay hard, I think, even if some other tasks are made easier. Mastering those challenges will still be where the utility and the pleasure of medicine will be found. The profession has adapted to waves of paradigm-shifting technological change before, yet the privilege of taking care of people remains.