JMIR Publications

ABSTRACT

Evaluation of large language model–based medical AI is increasingly centered on clinical decision-making benchmarks. Despite growing complexity in benchmark design, most frameworks remain anchored to outcome agreement—scoring models by conformity to predefined reference decisions. We argue that this paradigm is appropriate for assessing medical knowledge acquisition but becomes fundamentally misaligned once the objective shifts to clinical reasoning. Clinical decision-making rarely admits a single correct answer. Instead, it depends on structured reasoning under uncertainty, integrating probabilistic evidence, contextual constraints, and patient values. Using oncology as an illustrative domain, we show that decision variability is a structural feature of clinical reasoning rather than noise or error. Outcome-centric benchmarks therefore conflate reasonable divergence with incorrectness and incentivize answer imitation over transparent reasoning. We conclude that outcome agreement is mis-specified as a primary metric for clinical reasoning. Advancing medical AI beyond the knowledge phase requires shifting evaluation from answers to reasoning processes—a necessary condition for safe and trustworthy clinical deployment.