Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models

arXiv cs.CL / 4/30/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

共有:

Key Points

The article argues that existing medical benchmarks for large language models often fail to reflect clinical reality and may not manage data and safety requirements adequately.
It introduces MedCheck, a lifecycle-oriented assessment framework that breaks benchmark development into five continuous stages (from design to governance) and includes a 46-item medical, safety-focused checklist.
Using MedCheck, the authors evaluate 53 medical LLM benchmark efforts and identify systemic problems such as weak links to real-world clinical practice.
The study finds data integrity crises driven by contamination risks and a widespread omission of safety-critical evaluation aspects like robustness and uncertainty awareness.
MedCheck is positioned as both a diagnostic tool to audit current benchmarks and a practical guideline to help standardize more reliable and transparent AI evaluation in healthcare.

Abstract

Large language models (LLMs) show significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. However, concerns persist regarding the reliability of these benchmarks, which often lack clinical fidelity, robust data management, and safety-oriented evaluation metrics. To address these shortcomings, we introduce MedCheck, the first lifecycle-oriented assessment framework specifically designed for medical benchmarks. Our framework deconstructs a benchmark's development into five continuous stages, from design to governance, and provides a comprehensive checklist of 46 medically-tailored criteria. Using MedCheck, we conducted an in-depth empirical evaluation of 53 medical LLM benchmarks. Our analysis uncovers widespread, systemic issues, including a profound disconnect from clinical practice, a crisis of data integrity due to unmitigated contamination risks, and a systematic neglect of safety-critical evaluation dimensions like model robustness and uncertainty awareness. Based on these findings, MedCheck serves as both a diagnostic tool for existing benchmarks and an actionable guideline to foster a more standardized, reliable, and transparent approach to evaluating AI in healthcare.