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The paper introduces MedCheck, a lifecycle-oriented assessment framework with 46 medically-tailored criteria, to address the lack of clinical fidelity, data integrity, and safety evaluations in existing medical LLM benchmarks. They empirically evaluated 53 medical LLM benchmarks using MedCheck, revealing systemic issues such as disconnect from clinical practice, data contamination risks, and neglect of safety-critical evaluation dimensions. The findings highlight the need for a more standardized, reliable, and transparent approach to evaluating AI in healthcare.
Medical LLM benchmarks are riddled with issues like data contamination and a disconnect from real-world clinical practice, demanding a new standard for evaluation.
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.
