CLEAR用临床属性评估放射科报告，更准更懂医生想法。
CLEAR: A Clinically-Grounded Tabular Framework for Radiology Report Evaluation
- 构建表格框架，按五类关键属性逐项比对报告内容
- 在100份胸片报告上验证，与医生判断高度一致
- 适合评估报告生成模型的临床质量，尤其关注细节描述
现有评价指标往往缺乏细粒度和可解释性，难以捕捉候选报告与标准报告之间的细微临床差异。我们提出一种基于临床场景的表格化评估框架CLEAR，结合专家标注标签与属性级对比，不仅判断报告是否准确识别疾病存在与否，还评估其对阳性病灶在五个关键属性上的描述精度：首次出现、变化、严重程度、解剖位置及处理建议。相比以往方法，CLEAR的多维度属性级输出能更全面、更贴近临床地评估报告质量。为验证其临床相关性，我们联合五位认证放射科医生，构建CLEAR-Bench数据集，包含100份来自MIMIC-CXR的胸片报告，涵盖6个预定义属性与13种CheXpert疾病类别。实验表明，CLEAR在提取临床属性方面表现优异，其自动化指标与临床判断高度一致。
原文摘要 · Abstract (English)
Existing metrics often lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports, resulting in suboptimal evaluation. We introduce a Clinically-grounded tabular framework with Expert-curated labels and Attribute-level comparison for Radiology report evaluation (CLEAR). CLEAR not only examines whether a report can accurately identify the presence or absence of medical conditions, but also assesses whether it can precisely describe each positively identified condition across five key attributes: first occurrence, change, severity, descriptive location, and recommendation. Compared to prior works, CLEAR's multi-dimensional, attribute-level outputs enable a more comprehensive and clinically interpretable evaluation of report quality. Additionally, to measure the clinical alignment of CLEAR, we collaborate with five board-certified radiologists to develop CLEAR-Bench, a dataset of 100 chest X-ray reports from MIMIC-CXR, annotated across 6 curated attributes and 13 CheXpert conditions. Our experiments show that CLEAR achieves high accuracy in extracting clinical attributes and provides automated metrics that are strongly aligned with clinical judgment.
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