Medical Visual Question Answering

Medical visual question answering (MedVQA) asks models to interpret images such as X-rays, scans, pathology slides, and surgical videos in response to clinical questions, supporting tasks from finding specific abnormalities to integrating evidence across views or time. Common approaches adapt vision-language models with medical image-question datasets, while newer methods add region grounding, clinical knowledge, stepwise reasoning, or tools for measurements and image navigation. Benchmarks increasingly test spatial, temporal, multilingual, and multi-image reasoning, as well as whether answers actually depend on the visual evidence. A central challenge remains that high answer accuracy can coexist with shortcut use, hallucinations, poor calibration, or weak generalization across modalities and clinical settings.