What to Do Instead

Not yet is not the same as never. And it is definitely not the same as do nothing.

If autonomous AI is not the answer yet, but the doctor shortage is real, then the question becomes harder: what should we be doing right now? Here is where I land.

1. Use AI to free up the doctors we have

This is the idea from the first JAMA paper from earlier in the week (Martinelli et al., 2026). AI is at its strongest when it absorbs the work that pulled the doctor away from the patient in the first place — the paperwork, the notes, the inbox, the insurance forms.

Free up the doctors. Do not replace them. Same goal — more access to good care — different path. And the path with much better evidence behind it (Korom et al., 2025; Qazi et al., 2026).

2. Train every clinician to use AI well

Read the studies the JAMA authors cite carefully and one fact jumps out. The Pakistan trial showed huge gains for doctors trained to use AI. A US trial showed no gain when AI was dropped into clinics without training and workflow integration (Goh et al., 2024). Same tools. Different results.

Training is the leverage point. Right now, most doctors using AI in their practice are figuring it out alone. That is wasted gain. A national investment in teaching every clinician to use AI well would do more, faster, with less risk than any autonomous deployment.

3. Fix the workforce pipeline

Some of this is dull policy. More residency slots. Better paths for doctors trained in other countries. Less crushing administrative load on the ones we already have.

None of that is glamorous. None of it is AI-shaped. But the reason there are so few rural primary care doctors is not a lack of willing people. It is a system that grinds them down. AI can help with a piece of that. It cannot replace the work of fixing it.

4. Build the rules first, then issue the licenses

The licensing idea in the JAMA paper is genuinely good. I think it will be necessary one day. But the order they propose has the rules and the licenses arriving at the same time.

That is the wrong order.

First, build the rules. The malpractice law for AI. The clear path for a patient to be heard when something goes wrong. The disclosure standards so a person knows whether the entity in front of them is a human, an AI, or some mix. The independent monitoring of patient outcomes — not self-reported by the developer, but actually checked by an outside body.

Then, once those exist, issue the first licenses. Carefully. In small numbers. With patient outcomes tracked from day one.

5. Listen to what people actually want

This is the one most policy proposals miss. We have good evidence, in real surveys with thousands of people, about what patients want from medical AI. They want a clinician in the room. They want oversight from trusted bodies. They want to know when AI is being used (Bracic et al., 2026; Chen and Cui, 2025).

That is not a panel of experts speculating. That is patients answering directly. If the entire goal of expanding access is to take better care of people, the people themselves get a vote on how. So far, autonomous AI in primary care is not what they are voting for.

Where the line is

There is a sentence in the JAMA paper that I agree with completely:

As clinical AI increasingly resembles clinicians in its capabilities, our regulatory frameworks must evolve accordingly.

That is correct (Bergman et al., 2026). The frameworks have to evolve. I just do not think evolve yet means license to practice alone.

The careful version of progress here is something like this. Rush to make AI useful. Do not rush to make it autonomous. Use it to give the existing workforce more time and more reach. Build the rules and the trust before the licenses. And keep the human doctor in the room while we figure out — with evidence, not assumption — what AI alone can and cannot safely do.

If you are reading this and thinking about your own care, this article is policy commentary, not health advice. The right person to talk to about your medical decisions is your own doctor.

References

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

Bracic, A., Spector-Bagdady, K., Towle, S., Zhang, R., James, C. A., Price, W. N. II. (2026). Factors for Patient Trust and Acceptance of Medical Artificial Intelligence. JAMA Network Open, 9(3), e260815. doi:10.1001/jamanetworkopen.2026.0815

Chen, C., Cui, Z. (2025). Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals. Journal of Medical Internet Research, 27, e66083. doi:10.2196/66083

Goh, E., Gallo, R., Hom, J., et al. (2024). Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Network Open, 7(10), e2440969. doi:10.1001/jamanetworkopen.2024.40969

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

Martinelli, C., Carnevale, V., Ercoli, A., et al. (2026). Artificial Intelligence Is Not the End of the Physician. JAMA. doi:10.1001/jama.2026.4356

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