AI's confident answer may hide the rare condition it barely considered.

LLMs are built to narrow toward the typical, common answer.

Seeking the atypical clinical insight may add depth to the model.

Medical students learn one of medicine's oldest lessons almost as soon as they step onto their clinical rotations. It's even become part of folklore and sound advice. When you hear hoofbeats, think horses, not zebras.

Most patients have common diseases, and good clinicians learn to recognize familiar patterns before chasing unlikely ones.

But the counterpoint is also important. Medicine has always lived with a sort of tension between probability and possibility. Every experienced physician remembers a patient whose diagnosis arrived late, when everyone had accepted the first reasonable explanation and the zebra never got invited into the room. Interestingly, artificial intelligence may be creating a version of that same moment.

One Question, One Answer

More physicians are turning to large language models to support their practice—from data organization to clinical thinking. Most of those interactions follow the same path. A question goes in, and an answer comes back. The conversation often ends there—except for some conversational iteration, the model had said everything it had to say. But I'm not sure that assumption stands up to a little clinical and computational scrutiny. And I realize this can be a bit esoteric, but can also be important.

Large language models don't retrieve facts the way a textbook index does. One interesting variable behind how they generate a response is called........

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