RaTEScore: A Metric for Radiology Report Generation
Authors: name: Weike Zhao, name: Chaoyi Wu, name: Xiaoman Zhang, name: Ya Zhang, name: Yanfeng Wang, name: Weidi Xie

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.