Where Medical Responses First Go Wrong: Logic-Threaded Semantic Tree for Hallucination Detection and Correction

Hallucinations in clinical large vision-language model responses arise when claims are unsupported by medical images, patient context, or medical knowledge, and may further cascade toxically when subsequent claims depend on earlier erroneous statements. However, existing methods largely verify clinical claims independently, overlooking both inter-claim dependencies and the multimodal contextual evidence needed to assess each claim. To address these limitations, we propose MedFH, a unified framework for First-Hallucination-aware detection and correction. Specifically, MedFH explicitly models claim dependencies through logic threads over a unified semantic tree, which serves as a structural backbone for query and response sentences and claims. Building on this representation, MedFH subsequently verifies each claim against two complementary sources of evidence: grounding evidence obtained from external tools and predecessor evidence selected through logic threads. Based on the verification results, MedFH locates the first hallucinated claim and uses its direct logical successors as cascade proposals to guide revision from its source sentence onward. We also introduce MedLHallu-Bench, a multimodal benchmark for medical hallucination detection, first hallucination localization, and correction in radiology report generation and diagnostic reasoning. Experiments on MedLHallu-Bench show that MedFH consistently outperforms state-of-the-art baselines, and external evaluation further supports its effectiveness in correcting hallucinations in naturally generated responses. Code and datasets will be released.