A Proposed Rubric for Evaluating Expressed Clinical Reasoning in Large Language Model Responses
We propose a rubric for assessing expressed clinical reasoning in model responses, drawing on three bodies of work: medical education assessment frameworks (ART, SCT, Key Feature Problems and OSCE); clinical LLM benchmarks (MedR-Bench, HealthBench, TIMER-Bench, DR. BENCH, PrIME-LLM and PatientSafeBench); and general LLM reasoning evaluation research, including the Factuality-Validity-Coherence-Utility taxonomy, FaithCoT-Bench and C2-Faith. We use groundedness as a clinically oriented adaptation of the taxonomy's factuality category. The rubric brings these concepts together in a multidimensional framework for scoring free-text responses to gold-standard clinical vignettes. It includes pro...
| Authors | Zhangshu Joshua Jiang, Zina Ibrahim, James T. Teo |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 1 October 2026 |
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