MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language
  Models (VLMs) via Reinforcement Learning
Authors: name: Jiazhen Pan; name: Che Liu; name: Junde Wu; name: Fenglin Liu; name: Jiayuan Zhu; name: Hongwei Bran Li; name: Chen Chen; name: Cheng Ouyang; name: Daniel Rueckert
Published: 2025-02-26 23:57:34

Abstract: Reasoning is a critical frontier for advancing medical image analysis, where
transparency and trustworthiness play a central role in both clinician trust
and regulatory approval. Although Medical Visual Language Models (VLMs) show
promise for radiological tasks, most existing VLMs merely produce final answers
without revealing the underlying reasoning. To address this gap, we introduce
MedVLM-R1, a medical VLM that explicitly generates natural language reasoning
to enhance transparency and trustworthiness. Instead of relying on supervised
fine-tuning (SFT), which often suffers from overfitting to training
distributions and fails to foster genuine reasoning, MedVLM-R1 employs a
reinforcement learning framework that incentivizes the model to discover
human-interpretable reasoning paths without using any reasoning references.
Despite limited training data (600 visual question answering samples) and model
parameters (2B), MedVLM-R1 boosts accuracy from 55.11% to 78.22% across MRI,
CT, and X-ray benchmarks, outperforming larger models trained on over a million
samples. It also demonstrates robust domain generalization under
out-of-distribution tasks. By unifying medical image analysis with explicit
reasoning, MedVLM-R1 marks a pivotal step toward trustworthy and interpretable
AI in clinical practice.