By MANUEL PINILLA

Every few months a new study reports that an AI model has outperformed doctors on a set of diagnostic cases. The latest one I’ve read came out in Science in October: Microsoft’s MAI-DxO, tested on 304 complex cases from the New England Journal of Medicine and run as step-by-step simulations, reached the correct diagnosis in about 83% of them. Primary care doctors and general internists, working on their own with reference materials, got 36%. Microsoft says the next step is a study with real patients.

I build clinical AI, so these results matter to my work, but they answer a different question from mine. In the setting we work on, emergency departments and primary care, a doctor will be in the room, and that doctor signs the diagnosis and answers for it. While that is the case, a score of AI against doctors tells me little about whether a tool helps. What I need to know is whether the doctor, with the AI, gets the diagnosis right more often, and what happens when the AI is wrong.

There is a second gap. A published case arrives already written up: the history is complete, the relevant findings are there and the question is clear. A real patient tells the story out of order, leaves out what they don’t think matters and sometimes can’t put into words what they feel. Getting from that to a good differential is a large part of a doctor’s job, and a benchmark mostly skips it. How a tool behaves in a specific setting, with the information that setting really provides, matters as much as what the model knows.

Where the AI shows up in the consultation

I’m an engineer, not a physician, and when I think about these tools my first concern is the interaction more than the model. To use a chatbot during a consultation, a doctor has to stop, summarize the case, write a question, read the answer and judge how far to trust it before turning back to the patient. In a ten-minute primary care visit or a crowded emergency department there is rarely time for that, so in practice the tool tends to be consulted afterwards, to check a diagnosis that is already more or less settled. By then the patient has left, and with them the chance to ask a different question or examine something that was missed.

That is the problem we set out to work on at Medsys AI, a startup in Madrid. Our assistant listens to the consultation and, as it goes on, suggests diagnostic hypotheses, questions worth asking and things to check in the physical examination. The suggestions are updated as new information comes up, and the doctor doesn’t have to type anything. What to do with them is the doctor’s call. The picture I had in mind was an attending standing behind a resident who is unsure, available to doctors at any stage of their career.

A couple of design decisions follow from that. The assistant shows several hypotheses with the reasoning behind each, drawn from more than a thousand clinical guidelines and checked by more than one model before they reach the screen. And the system is built so that we can change the underlying model and rerun our evaluations when a better one appears.