ArticleCommunications medicine2024
Development of a large-scale medical visual question-answering dataset.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science.Scientific data · 2026Article
- MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- Developing and evaluating multimodal large language model for orthopantomography analysis to support clinical dentistry.Cell reports. Medicine · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Medical visual question answering with multimodal: a systematic mini review (2023-2026).Frontiers in digital health · 2026Review
- AI-assisted multimodal data integration for precision oncology.Frontiers in artificial intelligence · 2026Review
- A Benchmark for Breast Cancer Screening and Diagnosis in Mammogram Visual Question Answering.Nature communications · 2025Article
- ECSA: Mitigating Catastrophic Forgetting and Few-Shot Generalization in Medical Visual Question Answering.Tomography (Ann Arbor, Mich.) · 2025Article
- Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data.Nature communications · 2025Article
- Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study.PloS one · 2025Article
- Benchmarking large multimodal models for ophthalmic visual question answering with OphthalWeChat.Advances in ophthalmology practice and researchArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical Visual Question Answering (MedVQA) enhances diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret medical images. This study aims to redefine MedVQA as a generation task that mirrors human-machine interaction and to develop a model capable of integrating complex visual and textual information.
methodsWe constructed a large-scale medical visual-question answering dataset, PMC-VQA, containing 227,000 VQA pairs across 149,000 images that span various modalities and diseases. We introduced a generative model that aligns visual information from a pre-trained vision encoder with a large language model. This model was initially trained on PMC-VQA and subsequently fine-tuned on multiple public benchmarks.
resultsHere, we show that our model significantly outperforms existing MedVQA models in generating relevant, accurate free-form answers. We also propose a manually verified test set that presents a greater challenge and serves as a robust measure to monitor the advancement of generative MedVQA methods.
conclusionsThe PMC-VQA dataset proves to be an essential resource for the research community, and our model marks a significant breakthrough in MedVQA. We maintain a leaderboard to facilitate comprehensive evaluation and comparison, providing a centralized resource for benchmarking state-of-the-art approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.