AI RESEARCH
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
arXiv CS.LG
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ArXi:2604.02543v1 Announce Type: cross As vision-language models (VLMs) are increasingly deployed in clinical decision, 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.