SOURCE-LINKED INTELLIGENCE
RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching
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, se
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T16:05:17.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.