title: MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language
    Models (VLMs) via Reinforcement Learning
summary: 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.
paper_authors._id: 67c12bf3505a88e4a1866a01
paper_authors.name: Jiazhen Pan
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paper_authors.fullname: JZPeterPan
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paper_authors.name: Che Liu
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paper_authors.fullname: liu
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paper_authors.user: che111
paper_authors._id: 67c12bf3505a88e4a1866a03
paper_authors.name: Junde Wu
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paper_authors.fullname: Junde Wu
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paper_authors.user: morson
paper_authors._id: 67c12bf3505a88e4a1866a04
paper_authors.name: Fenglin Liu
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paper_authors.fullname: Fenglin Liu
paper_authors.type: user
paper_authors.user: fenglinliu
paper_authors._id: 67c12bf3505a88e4a1866a05
paper_authors.name: Jiayuan Zhu
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paper_authors.avatarUrl: /avatars/dc9a0c622f0509c5bc9bf82d8f6ad7e3.svg
paper_authors.fullname: Jiayuan Zhu
paper_authors.type: user
paper_authors.user: jiayuanz3
paper_authors._id: 67c12bf3505a88e4a1866a06
paper_authors.name: Hongwei Bran Li
paper_authors._id: 67c12bf3505a88e4a1866a07
paper_authors.name: Chen Chen
paper_authors._id: 67c12bf3505a88e4a1866a08
paper_authors.name: Cheng Ouyang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T17:49:25.580Z
paper_authors._id: 67c1e8cd73ae02d78044324b
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paper_authors.fullname: C O
paper_authors.type: user
paper_authors.user: ellivreksaB
paper_authors._id: 67c12bf3505a88e4a1866a09
paper_authors.name: Daniel Rueckert
paper_title: MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language
    Models (VLMs) via Reinforcement Learning
paper_summary: 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.
paper_ai_keywords: Visual Language Models (VLMs) radiological tasks spatial transparency trustworthiness clinician trust regulatory approval final answers underlying reasoning MedVLM-R1 medical VLM natural language reasoning supervised fine-tuning (SFT) overfitting overfitting to training distributions genuine reasoning human-interpretable reasoning paths reasoning references visual question answering samples model parameters control accuracy MRI CT X-ray benchmarks domain generalization out-of-distribution tasks trustworthy AI interpretable AI clinical outcomes