The New School

Chest X-ray vision-language models can write fluent reports that nonetheless assert findings absent from the image, and earlier fixes mostly retrained the generator through reinforcement learning, preference optimization or chain-of-thought supervision. CheXPoT instead works after generation: it decomposes a report into checkable predicates, tests each against a visual evidence table built from a chest X-ray classifier and Grad-CAM locations, and rewrites the report after removing predicates the image cannot support. On a 4,145-image held-out split, MedGemma-4B reports moved from 19.2% to 46.8% micro-average precision and from 23.0% to 49.4% micro-average F1, with significant precision gains on 8 of 14 CheXpert classes and no significant decreases in precision or F1.

Chest X-ray vision-language models can write fluent reports that nonetheless assert findings absent from the image, and earlier fixes mostly retrained the generator through reinforcement learning, preference optimization or chain-of-thought supervision. CheXPoT instead works after generation: it decomposes a report into checkable predicates, tests each against a visual evidence table built from a chest X-ray classifier and Grad-CAM locations, and rewrites the report after removing predicates the image cannot support. On a 4,145-image held-out split, MedGemma-4B reports moved from 19.2% to 46.8% micro-average precision and from 23.0% to 49.4% micro-average F1, with significant precision gains on 8 of 14 CheXpert classes and no significant decreases in precision or F1.