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sequential-diagnosis

topic1 events
papersSEP 10 04:00 UTC

Study examines when clinical AI agents should stop testing and commit to a diagnosis

A new arXiv paper addresses the stopping problem for AI agents in clinical diagnosis, which must decide when to request another test, when to commit to a diagnosis, and when to defer. The authors note that current agent benchmarks typically measure accuracy under fixed or unconstrained interaction, leaving the reliability of autonomous stopping untested. They propose a risk-constrained framework for evaluating and controlling these stopping decisions in sequential diagnosis settings.

agents/healthcare/safety/benchmarks