RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review

As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language model (LLM)-based metrics are now the best-correlated with radiologist judgment, yet they output a single opaque score that neither a clinician nor a model builder can easily interpret or audit. We introduce RadMatch, a multi-stage, LLM-based metric that decomposes report comparison into a structured finding-level matching with significance-aware scoring and error characterization across seven clinical attribute dimensions (status, location, severity, morphology, certainty, longitudinal comparison, and measurement). The main score is the actionable-error count, both interpretable and auditable. Candidate findings are graded correct, partial, or incorrect, and unmatched findings are counted as missed or hallucinated. Triage and actionable safety recall/precision and per-subset views add complementary, deployment-oriented lenses. Across two expert benchmarks, RadMatch is the most clinically aligned metric, matching inter-radiologist agreement on ReXVal and more than doubling the best prior metric on the harder RadEvalExpert. Relying only on few-shot prompting, it is designed to extend to other modalities and anatomies. We will release RadMatch as open-source code with an interactive dashboard for inspecting results.

Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone. The most direct opportunity is reducing the time and effort radiologists spend producing reports, a task that requires interpreting images, integrating clinical history and prior studies, and drafting structured findings. We present Harrison.Rad 1.5 (HR1.5), a radiology-specific multimodal large language model that accepts interleaved text and visual inputs and generates structured and unstructured text across plain-film radiology, spanning computed radiography, chest, musculoskeletal, abdominal, spine, and pelvic x-rays, and mammography. HR1.5 is trained through a three-stage pipeline: domain adaptation of a base language model on radiology reports, contrastive vision-encoder training with curriculum-based hard negatives on ~6 million image-report instances, and visual-question-answering fine-tuning on multi-turn conversations. We evaluate it with a Findings-Diagnosis scoring framework that extends RadGraph-XL entity extraction with ontology-based synonym matching and polarity-contradiction detection, benchmarked on RadBench, a simulated FRCR 2B Short Case examination scored against Angoff-method thresholds, ReXGradient, and internal multi-modality datasets. HR1.5 is the only system evaluated to meet the simulated FRCR passing standard and achieves the highest accuracy on closed-format clinical questions, across anatomical regions, on internal multi-body-part and mammography reporting, and on the primary clinically-aligned score for public chest reporting. We further examine explainability and model behaviour, including question-sensitive Grad-CAM heatmaps, attention analysis, and confidence estimation, to support responsible future evaluation toward clinical use, and a framework for clinically grounded assessment of report quality.

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.