Towards Better Aligned LLM Judges for Radiology Report Evaluation across Modalities and Anatomies

Abstract

Current work on radiology report evaluation has focused primarily on LLM-based metrics for chest X-rays (CXR), leaving open whether such approaches remain robust across other modalities and anatomies, and which model and prompt configurations are best suited to serve as LLM judges. We present a comprehensive evaluation of LLM judges for radiology report evaluation across diverse model families, prompting strategies, and adaptation methods, leveraging two expert-annotated datasets that span multiple imaging modalities and anatomical regions. Specifically, we compare four existing metrics (RadFact, GREEN, FineRadScore, and CRIMSON) with our proposed metric, VERT, across open- and closed-source models of varying sizes and reasoning capabilities. Among existing metrics, GREEN proves the most robust, outperforming more recent CXR-specific approaches. VERT further improves Kendall's correlation with radiologist judgment by up to 26% over GREEN, while ensembling and lightweight fine-tuning of Qwen3-30B yield gains of up to 9.9% and 25%, respectively, of which the latter also has a latency reduction of 37.2x. A systematic error analysis further shows that, while these metrics correlate with expert ratings overall, they tend to miscount errors such as omissions. Overall, our results show that carefully selected general-purpose LLM judges provide strong radiology report evaluation across modalities, with lightweight adaptation offering complementary gains when annotated data is available.