MedRAX: Medical Reasoning Agent for Chest X-ray
This addresses the practical utility gap in clinical CXR interpretation by providing a unified framework, though it is incremental as it builds on existing models.
The paper tackles the problem of isolated CXR interpretation models by introducing MedRAX, a versatile AI agent that integrates existing tools and multimodal LLMs to address complex medical queries without additional training, achieving state-of-the-art performance on a new benchmark of 2,500 queries.
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