How Clinicians Think and What AI Can Learn From It
Quick summary
arXiv:2601.12547v2 Announce Type: replace Abstract: Clinical artificial intelligence increasingly builds high-dimensional representations of patients, yet every finite clinical model is an abstraction. The key question is not only how accurately a model predicts, but which distinctions need to be represented, at what resolution, for the decision at hand. We argue that purposive clinical AI should use decision-sufficient abstraction: model detail should be conditioned by the objective structure of the decision and limited by the evidence available to support that detail. We further argue that t
Key takeaways
- arXiv:2601.12547v2 Announce Type: replace Abstract: Clinical artificial intelligence increasingly builds high-dimensional representations of patients, yet every finite clinical model is an abstraction.
- The key question is not only how accurately a model predicts, but which distinctions need to be represented, at what resolution, for the decision at hand.
- We argue that purposive clinical AI should use decision-sufficient abstraction: model detail should be conditioned by the objective structure of the decision and limited by the evidence available to support that detail.
Why it matters
“How Clinicians Think and What AI Can Learn From It” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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