OpenMedQ: New Open-Source Medical Vision-Language Model Achieves State-of-the-Art Performance
Researchers have developed OpenMedQ, an open-source medical vision-language model pretrained on 3.35 million samples across pathology, radiology, microscopy, and clinical QA datasets. The model outperforms much larger proprietary models like Med-PaLM M on medical visual question-answering tasks while being 80 times smaller. The release of open-source code and interactive demo aims to establish a reproducible baseline for the medical AI research community.
OpenMedQ represents a significant advancement in open medical AI by combining 14 datasets totaling approximately 3.35 million pretraining samples spanning multiple medical imaging domains and clinical text. The model achieves state-of-the-art BLEU-1 scores on PathVQA (75.9) and matches top performance on VQA-MED (64.5), notably surpassing Med-PaLM M variants that are up to 562 billion parameters—approximately 80 times larger. When its vision encoder was transferred to eight unseen medical classification benchmarks, it achieved the highest average macro-F1 score (0.757) compared to other biomedical CLIP variants including BiomedCLIP, PMC-CLIP, and PubMedCLIP. The researchers have released their code and made an interactive demo publicly available, positioning OpenMedQ as a reproducible baseline for future medical vision-language research. This work was accepted to the Medical Imaging with Deep Learning (MIDL) 2026 conference short paper track.
What different sources said
- arXiv cs.AICenter
OpenMedQ: Broad Open Pretraining for Medical Vision-Language Models
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