MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement

2026-01-16

AI Summary

This work addresses the limitations of existing medical vision-language models in complex clinical reasoning tasks, which stem from the scarcity of deep reasoning data, challenges in aligning knowledge across multiple specialties, and the inability of standard reinforcement learning to capture the diversity of diagnostic logic. To overcome these issues, the authors construct a multi-specialty dataset comprising 10K samples with annotated reasoning trajectories and introduce three key technical innovations: domain-specific LoRA modules with diverse initialization, a clinical guideline–informed advantage function to model heterogeneous diagnostic reasoning, and a conflict-aware fusion strategy to integrate multi-specialty expert knowledge. The proposed approach achieves state-of-the-art performance, reporting scores of 27.50 on MedXpert-MM and 83.03 on OmniMedVQA, thereby establishing a foundation for reliable multimodal medical reasoning systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Medical Vision-Language Models (MedVLMs) excel at perception tasks but struggle with complex clinical reasoning required in real-world scenarios. While reinforcement learning (RL) has been explored to enhance reasoning capabilities, existing approaches face critical mismatches: the scarcity of deep reasoning data, cold-start limits multi-specialty alignment, and standard RL algorithms fail to model clinical reasoning diversity. We propose MMedExpert-R1, a novel reasoning MedVLM that addresses these challenges through domain-specific adaptation and clinical guideline reinforcement. We construct MMedExpert, a high-quality dataset of 10K samples across four specialties with step-by-step reasoning traces. Our Domain-Specific Adaptation (DSA) creates specialty-specific LoRA modules to provide diverse initialization, while Guideline-Based Advantages (GBA) explicitly models different clinical reasoning perspectives to align with real-world diagnostic strategies. Conflict-Aware Capability Integration then merges these specialized experts into a unified agent, ensuring robust multi-specialty alignment. Comprehensive experiments demonstrate state-of-the-art performance, with our 7B model achieving 27.50 on MedXpert-MM and 83.03 on OmniMedVQA, establishing a robust foundation for reliable multimodal medical reasoning systems.

Problem

• Medical Vision-Language Models
• Multimodal Medical Reasoning
• Clinical Reasoning
• Reinforcement Learning
• Multi-specialty Alignment

Innovation

• Domain-Specific Adaptation
• Clinical Guideline Reinforcement
• Medical Vision-Language Model
• Multimodal Medical Reasoning
• LoRA