---
license: apache-2.0
datasets:
- foreverbeliever/OmniMedVQA
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2-VL-2B-Instruct
pipeline_tag: visual-question-answering
---
# Med-R1
Med-R1 is a reinforcement learning (RL)-enhanced vision-language model (VLM) designed for medical reasoning across 8 imaging modalities (CT, MRI, Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography (OCT), Microscopy, and X-ray) and 5 key tasks (modality recognition, anatomy identification, disease diagnosis, lesion grading, and biological attribute analysis). Using Group Relative Policy Optimization (GRPO), Med-R1 improves generalization and trustworthiness, surpassing Qwen2-VL-2B by 29.94% and even outperforming the much larger Qwen2-VL-72B. Our model checkpoints provide researchers with a powerful tool for advancing medical AI with RL-driven enhancements.

## Description of Models

- **Cross-Modality**: We provide checkpoints trained separately on the following modalities:
  - **CT**, **MRI**, **X-Ray**, **Fundus (FP)**, **Dermoscopy (Der)**, **Microscopy (Micro)**, **OCT**, and **Ultrasound (US)**.

- **Cross-Task Learning**: We provide checkpoints trained separately on the following tasks:
  - **Anatomy Identification**, **Disease Diagnosis**, **Lesion Grading**, **Modality Recognition**, and **Biological Attribute Analysis**.

## Citation