MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language
  Models (VLMs) via Reinforcement Learning
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.
67c12bf3505a88e4a1866a01
Jiazhen Pan
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2025-03-02T20:18:10.120Z
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JZPeterPan
user
JZPeterPan
67c12bf3505a88e4a1866a02
Che Liu
claimed_verified
2025-02-28T09:28:38.598Z
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liu
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che111
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Junde Wu
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2025-02-28T12:14:18.528Z
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Junde Wu
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morson
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Fenglin Liu
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2025-02-28T12:23:44.550Z
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Fenglin Liu
user
fenglinliu
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Jiayuan Zhu
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2025-02-28T15:14:46.124Z
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Jiayuan Zhu
user
jiayuanz3
67c12bf3505a88e4a1866a06
Hongwei Bran Li
67c12bf3505a88e4a1866a07
Chen Chen
claimed_verified
2025-06-16T12:56:38.901Z
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Chen Chen
user
cherisechen
67c12bf3505a88e4a1866a08
Cheng Ouyang
admin_assigned
2025-02-28T17:49:25.580Z
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C O
user
ellivreksaB
67c12bf3505a88e4a1866a09
Daniel Rueckert
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language
  Models (VLMs) via Reinforcement Learning
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.
MedVLM-R1, a reinforcement learning-based medical VLM, enhances transparency and trust in medical image analysis by generating natural language reasoning, improving accuracy and domain generalization with limited data.
Medical Visual Language Models VLMs reinforcement learning human-interpretable reasoning visual question answering MRI CT X-ray benchmarks domain generalization