Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

• URL: http://arxiv.org/abs/2503.13939v3
• Date: Sat, 29 Mar 2025 16:37:10 GMT
• Title: Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models
• Authors: Yuxiang Lai, Jike Zhong, Ming Li, Shitian Zhao, Xiaofeng Yang,
• Abstract summary: Vision-language models (VLMs) have advanced reasoning in natural scenes, but their role in medical imaging remains underexplored.<n>We introduce Med-R1, a framework exploring reinforcement learning to enhance VLMs' generalizability and trustworthiness in medical reasoning.<n>Med-R1 is evaluated across eight medical imaging modalities: CT, MRI, Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography, Microscopy, and X-ray Imaging.
• Score: 6.176432104264649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have advanced reasoning in natural scenes, but their role in medical imaging remains underexplored. Medical reasoning tasks demand robust image analysis and well-justified answers, posing challenges due to the complexity of medical images. Transparency and trustworthiness are essential for clinical adoption and regulatory compliance. We introduce Med-R1, a framework exploring reinforcement learning (RL) to enhance VLMs' generalizability and trustworthiness in medical reasoning. Leveraging the DeepSeek strategy, we employ Group Relative Policy Optimization (GRPO) to guide reasoning paths via reward signals. Unlike supervised fine-tuning (SFT), which often overfits and lacks generalization, RL fosters robust and diverse reasoning. Med-R1 is evaluated across eight medical imaging modalities: CT, MRI, Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography (OCT), Microscopy, and X-ray Imaging. Compared to its base model, Qwen2-VL-2B, Med-R1 achieves a 29.94% accuracy improvement and outperforms Qwen2-VL-72B, which has 36 times more parameters. Testing across five question types-modality recognition, anatomy identification, disease diagnosis, lesion grading, and biological attribute analysis Med-R1 demonstrates superior generalization, exceeding Qwen2-VL-2B by 32.06% and surpassing Qwen2-VL-72B in question-type generalization. These findings show that RL improves medical reasoning and enables parameter-efficient models to outperform significantly larger ones. By demonstrating strong cross-domain and cross-task performance, Med-R1 points toward a new direction for developing practical and generalizable medical VLMs.

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