FirsthandTech
arXiv — cs.AI preprintsInternational16 October 2026

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

This is an official announcement record

Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.

arXiv:2604.03376v2 Announce Type: replace 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. S
— arXiv — cs.AI preprints

More from arXiv — cs.AI preprints

This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.