OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

• URL: http://arxiv.org/abs/2606.12169v1
• Date: Wed, 10 Jun 2026 14:56:51 GMT
• Title: OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models
• Abstract summary: We introduce OpenMedReason, a large-scale, open multimodal medical reasoning corpus.<n>It covers diverse medical domain vision modalities such as radiological scans, microscopic images, visible light photographs, charts, and others.<n>Training with OpenMedReason yields a 20% average improvement in VQA accuracy over the base model.
• Score: 45.337436216670746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers. We introduce OpenMedReason, a large-scale, open multimodal medical reasoning corpus comprising approximately 450K image-question-answer instances whose reasoning traces are primarily derived from curated biomedical, human-authored scientific articles. OpenMedReason provides high-fidelity supervision beyond synthetic chains of thought, covering diverse medical domain vision modalities such as radiological scans, microscopic images, visible light photographs, charts, and others. We complement it with OpenMedReason-Bench, a held-out benchmark that allows fine-grained evaluation of LVLMs along three complementary axes of capability, including perception, medical knowledge, and rationale, enabling diagnostic evaluation beyond final-answer accuracy. OpenMedReason is a rich training resource that exhibits its effectiveness in both supervised fine-tuning (SFT) and reinforcement-based alignment. Training with OpenMedReason yields a 20% average improvement in VQA accuracy over the base model and achieves performance within 4.2% of the strongest comparable-scale medical LVLMs. Fine-grained performance analysis confirms that the gains are not concentrated in any single axis: OpenMedReason improves perception, medical knowledge, and rationale jointly, and its reasoning traces are preferred over those of the base model in 86.1% of pairwise comparisons. We release the code and dataset at huggingface.co/datasets/neginb/OpenMedReason.

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