Authors
Jiawei Zhou, Hanqing He, Weijie Yu, Zihan Zhong, Qiang Ju, Shuhang Wang, Xiaochuan Wang, Ning Li
Published in
Journal of medical Internet research. Volume 28. Pages e99019. Nov 13, 2026. Epub Nov 13, 2026.
Abstract
This viewpoint addresses developers and evaluators of large language model (LLM)-based clinical decision support systems. Although recent benchmarks increasingly evaluate multiple clinical reasoning domains, many still rely substantially on reference decision agreement-whether model outputs conform to predefined decisions. Our aim is to clarify when such agreement remains useful, when it becomes insufficient for complex or preference-sensitive decisions, and how it can be complemented by reasoning-centered assessment. Clinical decision-making often depends on structured reasoning under uncertainty, integrating probabilistic evidence, contextual constraints, and patient values. Using oncology as an illustrative domain, we argue that some decision variability is a structural feature of clinical reasoning rather than noise or error. Used alone in such settings, single-reference benchmarks may conflate reasonable divergence with incorrectness and reward answer imitation over transparent reasoning. We conclude that reference decision agreement should be retained for tasks with stable ground truth but supplemented by evidence grounding, reasoning quality, patient-centeredness, and safety checks for open-ended clinical decision support. This multidimensional approach is intended to support safer and more clinically meaningful evaluation.
PMID:
43024856
Bibliographic data and abstract were imported from PubMed on 14 Nov 2026.
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