AtomiMed：面向通用临床感知的医学报告评估的分层原子事实核查

摘要

传统的医学报告生成（MRG）评估指标主要依赖于基于表层的n-gram重叠，这无法捕捉临床事实的准确性，且常常忽略了灾难性的诊断错误。为了解决这一根本性局限，我们提出了AtomiMed——一种通用的、模态无关的评估框架，该框架将复杂的医学叙述分解为标准化、多层次的“原子临床事实”层级结构，涵盖疾病级实体和属性级描述（包括位置、形态和严重程度）。通过在真实报告与预测报告之间实现智能交叉验证循环，AtomiMed模拟了多放射科医师的同行评议过程，以验证临床一致性，从而实现对诊断检测与描述准确性的解耦评估。为促进标准化评估，我们推出了MRGEvalKit——一个用于自动化层次化提取的开源工具包，并整理了OmniMRG-Bench——一个覆盖X光、CT、MRI和超声的综合多模态基准。在多个专家标注的读者研究中进行的广泛实验表明，与传统指标及基于模型的指标相比，AtomiMed与人类放射科医师判断的相关性显著更高。我们的代码已发布在 https://github.com/Venn2336/MRGEvalkit。

English

Traditional metrics for Medical Report Generation (MRG) predominantly rely on surface-level n-gram overlap, which fails to capture clinical factual accuracy and often overlooks catastrophic diagnostic errors. We address this fundamental limitation by proposing AtomiMed, a universal, modality-agnostic evaluation framework that decomposes complex medical narratives into a standardized, multi-level hierarchy of Atomic Clinical Facts, encompassing Disease-level entities and Attribute-level descriptors, including location, morphology, and severity. By implementing an Agentic Cross-Verification loop between ground-truth and predicted reports, AtomiMed simulates a multi-radiologist peer-review process to verify clinical consistency, thus enabling the decoupled assessment of diagnostic detection and descriptive accuracy. To facilitate standardized evaluation, we introduce MRGEvalKit, an open-source toolkit for automated hierarchical extraction, and curate OmniMRG-Bench, a comprehensive multi-modal benchmark covering X-ray, CT, MRI, and Ultrasound. Extensive experiments on multiple expert-annotated reader studies demonstrate that AtomiMed achieves significantly higher correlation with human radiologist judgment compared to traditional and model-based metrics. Our code are release at https://github.com/Venn2336/MRGEvalkit