Decide, Ask, or Defer: Clinical LLMs under Incomplete Evidence

Abstract:Clinical LLMs must decide not only what diagnosis to produce, but also whether the available evidence is sufficient for autonomous decision making. Binary DECIDE/ABSTAIN formulations merge distinct non decision states and do not explicitly evaluate information acquisition. We introduce a DECIDE/ASK/DEFER formulation together with a blinded protocol that prevents models from using evidence completeness metadata. We evaluate Qwen, Gemini, and GPT on 200 matched clinical evidence states constructed from DDXPlus. The models show substantial differences in action selection under identical evidence, with disagreement in 137 of 200 states. For Qwen, a matched targeted versus random analysis shows that selected information changes the likelihood of a subsequent autonomous decision more clearly than diagnostic correctness. Its matched DECIDE/ABSTAIN baseline further reveals a safety autonomy tradeoff: the three action policy rescues some erroneous autonomous decisions but also removes some correct autonomous deci sions. These results show that separating information acquisition from clinician deferral exposes behavior that binary abstention hides, without yielding a uniformly improved decision policy.

Preprint. Accepted to the Main Track of the NeurIPS 2026 GenAI4Health Workshop

Artificial Intelligence (cs.AI)

arXiv:2610.04542 [cs.AI]

(or arXiv:2610.04542v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.04542 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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