MedRAX: Medical Reasoning Agent for Chest X-ray
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
Chest X-rays (CXRs) play an integral role in driving critical decisions in
disease management and patient care. While recent innovations have led to
specialized models for various CXR interpretation tasks, these solutions often
operate in isolation, limiting their practical utility in clinical practice. We
present MedRAX, the first versatile AI agent that seamlessly integrates
state-of-the-art CXR analysis tools and multimodal large language models into a
unified framework. MedRAX dynamically leverages these models to address complex
medical queries without requiring additional training. To rigorously evaluate
its capabilities, we introduce ChestAgentBench, a comprehensive benchmark
containing 2,500 complex medical queries across 7 diverse categories. Our
experiments demonstrate that MedRAX achieves state-of-the-art performance
compared to both open-source and proprietary models, representing a significant
step toward the practical deployment of automated CXR interpretation systems.
Data and code have been publicly available at
https://github.com/bowang-lab/MedRAX
