The Case the Authors Make

Before disagreeing with someone smart, you have to be able to make their argument better than they made it. That is the rule. Otherwise you are just yelling at a version of the argument you made up yourself.

So let me try.

The shortage is real, and it is getting worse

The United States is short of doctors. Projections in the JAMA paper point to tens of thousands of physician shortfall in the coming decade (Bergman et al., 2026). The reasons are simple. People are getting older. Older people need more care. Doctors are leaving the job faster than new ones are arriving. Rural areas are hit the hardest.

The authors put the problem plainly. Building more doctors takes time. Medical school is four years. Residency is three to seven more. By the time we train our way out of this shortage, a generation of patients will already have gone without.

People in those rural counties are not waiting for a policy paper. They are waiting for someone — anyone — to look at the rash, listen to the cough, refill the prescription.

The AI has actually gotten good

This is the part that is easy to miss. AI in medicine has changed in the last two or three years. It is not what it was.

The paper cites a 2025 study from Kenya that ran AI-supported decision tools across nearly 40,000 primary care visits (Korom et al., 2025). The visits with AI support had fewer wrong diagnoses and fewer wrong treatments than the visits without.

It cites another study from Pakistan in early 2026 — a true randomized trial, the kind we trust most. Doctors trained to use large language models (the kind of AI behind tools like ChatGPT) had much better diagnostic reasoning than doctors who were not trained to use them (Qazi et al., 2026).

And it cites the NOHARM study, posted in late 2025, which compared the strongest current language model to human physicians on safety and on completeness of clinical responses. The result, in the authors' own words:

The strongest large language model outperformed physicians on safety… clinicians not outperforming large language models on any metric.

That is a real result (Wu et al., 2025). The AI was safer than the doctors on the test. Read it again.

The licensing idea is more careful than it sounds

If you only read the headline, license AI to practice medicine sounds reckless. Reading the actual proposal, it is more careful than that.

The authors say the AI would have to:

Pass standardized exams. Score at or above the median of recent human test takers on all three parts of the US Medical Licensing Examination.

Do a residency. Enter a supervised deployment period before being trusted to work alone — same idea as a new doctor.

Stay in its lane. Be allowed to do certain things (gather histories, recommend next steps in primary care) and not others (prescribe certain medicines without a clinician approving).

Renew its license. Every two years or so, prove again that it is still safe.

Be subject to discipline. If outcomes go bad, the license can be restricted, suspended, or taken away.

That is not reckless. That is more like how we license a doctor. Or a teacher. Or a driver. Pass a test. Do supervised practice. Stay within a scope. Get checked again later.

Where this argument actually stands

So we have a real shortage, real new AI ability, and a real proposal for how to put guardrails on autonomous use. None of those three are nothing. Anyone who waves the whole thing away has not read the paper.

And yet.

The next chapter is the six things I keep coming back to. The reasons that, even after granting all of this, I do not think the world is ready for an AI doctor without a human doctor in the room.

References

Bergman, A., Wachter, R. M., Emanuel, E. J. (2026). A Licensure Framework for Autonomous Clinical AI. JAMA. doi:10.1001/jama.2026.5483

Korom, R., Kiptinness, S., Adan, N., et al. (2025). AI-based clinical decision support for primary care: a real-world study. arXiv. doi:10.48550/arXiv.2507.16947

Qazi, I. A., Ali, A., Khawaja, A. U., et al. (2026). Large language model diagnostic assistance for physicians in a lower-middle-income country: a randomized controlled trial. Nature Health, 1(2), 198–205. doi:10.1038/s44360-025-00007-8

Wu, D., Haredasht, F. N., Maharaj, S. K., et al. (2025). First, do NOHARM: towards clinically safe large language models. arXiv. doi:10.48550/arXiv.2512.01241