Automated grounding verification of AI statements about chest radiographs

The paper presents BRACE, an automated framework that builds training examples from public radiology reports and Radiopaedia articles to check a statement about a patient's image against both the report and the cited evidence. Across 42 verifiers (0.1–284B parameters) on five public chest radiograph datasets, verifiers trained with BRACE accepted a mean of 0.239 of AI-written statements contradicted by the report at a threshold accepting 0.90 of supported examples, versus 0.830 for verifiers trained without contradicted examples, and accepted 0.169 of statements with an agreeing report but unsupporting citation, versus 0.830 for zero-shot LLMs. Two board-certified radiologists' independent labels agreed with the framework's labels (k > 0.910) nearly as closely as with each other (k = 0.952), and a recipe food-allergen instantiation accepted 0.493 of contradicted statements versus 0.898 for a lexical baseline.