This paper introduces a novel, entity-aware metric, termed as Radiological Report (Text) Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models. RaTEScore emphasizes crucial medical entities such as diagnostic outcomes and anatomical details, and is robust against complex medical synonyms and sensitive to negation expressions. Technically, we developed a comprehensive medical NER dataset, RaTE-NER, and trained an NER model specifically for this purpose. This model enables the decomposition of complex radiological reports into constituent medical entities. The metric itself is derived by comparing the similarity of entity embeddings, obtained from a language model, based on their types and relevance to clinical significance. Our evaluations demonstrate that RaTEScore aligns more closely with human preference than existing metrics, validated both on established public benchmarks and our newly proposed RaTE-Eval benchmark.

翻译：本文提出了一种新颖的、具备实体感知能力的评估指标，称为放射学报告（文本）评估（RaTEScore），用于评估AI模型生成的医学报告质量。RaTEScore强调关键的医学实体，如诊断结果和解剖学细节，并对复杂的医学同义词具有鲁棒性，同时对否定表达敏感。在技术上，我们构建了一个全面的医学命名实体识别数据集RaTE-NER，并专门为此训练了一个NER模型。该模型能够将复杂的放射学报告分解为构成其的医学实体。该指标本身是通过比较从语言模型获得的实体嵌入的相似性得出的，比较基于实体的类型及其与临床意义的相关性。我们的评估表明，无论是在已建立的公共基准测试上，还是在我们新提出的RaTE-Eval基准测试上，RaTEScore都比现有指标更符合人类偏好。