Chest computed tomography (CT) reports generated by large language models (LLMs) can describe positive findings that lack localized evidence in the scan.
We propose EviGround-CT to verify these findings after report generation.
A deterministic extractor identifies positive finding sentences, and a 3D grounding model predicts an evidence mask for each sentence.
Using the full sentence retains anatomical location, laterality, and descriptive modifiers in the grounding query.
We evaluate report generation on CT-RATE, and grounding and verification on a held-out 100-case ReXGroundingCT subset.
Using the available annotations, sentence-grounded verification raises evidence-supported precision (ESP) from 0.263 to 0.365 across 137 extracted in-scope positives, compared with category conditioning.
The corresponding unsupported positive finding rate falls from 0.737 to 0.635.
The paired case-level bootstrap ESP difference is +0.102 (95% confidence interval: [0.028, 0.181]).
The predicted masks associate individual report statements with spatial evidence that can be inspected alongside the CT.