Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation

Excerpt

arXiv:2604.02543v2 Announce Type: replace-cross Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their predictions is equally critical. Yet, a comprehensive and systematic investigation into the overconfidence of these models remains notably scarce in the medical domain. We address this gap through a comprehensive empirical study of confidence calibration in VLMs, spanning three model families