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Medical Visual Question Answering: A Survey

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arxiv 2111.10056 v3 pith:LLTCWY4C submitted 2021-11-19 cs.CV cs.AI

classification cs.CVcs.AI
keywords medicalquestionansweringdiscussfieldpartvisualchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MIRA: A Novel Framework for Fusing Modalities in Medical RAG

    cs.CV 2025-07 reject novelty 4.0 of 10

    A medical multimodal RAG pipeline with rethink-and-rearrange and online search; the claimed SOTA is contradicted by the paper's own PMC-VQA numbers.

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