Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty

arXiv:2606.28520v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly used for clinical image understanding, yet they remain vulnerable to \emph{hallucinations}--producing textual findings or attributes not supported by the image. We present a vision-traceable hallucination detection framework that audits arbitrary LVLM responses via visual evidence grounding, requiring neither modification nor internal access to the hidden states of LVLMs. Given an LVLM response,...