AI RESEARCH
InViC: Intent-aware Visual Cues for Medical Visual Question Answering
arXiv CS.CV
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ArXi:2603.16372v1 Announce Type: new Medical visual question answering (Med-VQA) aims to answer clinically relevant questions grounded in medical images. However, existing multimodal large language models (MLLMs) often exhibit shortcut answering, producing plausible responses by exploiting language priors or dataset biases while insufficiently attending to visual evidence. This behavior undermines clinical reliability, especially when subtle imaging findings are decisive.