ReEvalMed：通过对齐指标与现实临床判断重新思考医学报告评估
ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment
AI总结：
针对现有放射学报告评估指标得分与临床信任脱节的问题，研究提出ReEvalMed临床导向元评估框架，通过细粒度标注数据集系统揭示现有指标的临床语义解读缺陷，为构建更可靠的临床评估方法提供指导。
摘要：
自动生成的放射学报告往往在现有评估指标中获得高分，却无法赢得临床医生的信任。这一差距暴露了当前指标评估生成报告质量的根本缺陷。我们重新思考了这类指标的设计与评估方式，提出了一个基于临床的元评估框架。我们定义了涵盖临床对齐性以及判别性、鲁棒性、单调性等核心指标能力的临床导向标准。借助一个标注了错误类型、临床显著性标签及解释的、包含真实报告与改写报告对的细粒度数据集，我们系统评估了现有指标，揭示了它们在解读临床语义方面的局限性，比如无法区分具有临床意义的错误、过度惩罚无害的表述差异、在不同错误严重程度间缺乏一致性。我们的框架为构建更具临床可靠性的评估方法提供了指导。
英文摘要：
Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians' trust. This gap reveals fundamental flaws in how current metrics assess the quality of generated reports. We rethink the design and evaluation of these metrics and propose a clinically grounded Meta-Evaluation framework. We define clinically grounded criteria spanning clinical alignment and key metric capabilities, including discrimination, robustness, and monotonicity. Using a fine-grained dataset of ground truth and rewritten report pairs annotated with error types, clinical significance labels, and explanations, we systematically evaluate existing metrics and reveal their limitations in interpreting clinical semantics, such as failing to distinguish clinically significant errors, over-penalizing harmless variations, and lacking consistency across error severity levels. Our framework offers guidance for building more clinically reliable evaluation methods.
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