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.
MedRAX is built on a robust technical foundation:
- Core Architecture: Built on LangChain and LangGraph frameworks
- Language Model: Uses GPT-4o with vision capabilities as the backbone LLM
- Deployment: Supports both local and cloud-based deployments
- Interface: Production-ready interface built with Gradio
- Modular Design: Tool-agnostic architecture allowing easy integration of new capabilities
- Visual QA: Utilizes CheXagent and LLaVA-Med for complex visual understanding and medical reasoning
- Segmentation: Employs MedSAM and PSPNet model trained on ChestX-Det for precise anatomical structure identification
- Grounding: Uses Maira-2 for localizing specific findings in medical images
- Report Generation: Implements SwinV2 Transformer trained on CheXpert Plus for detailed medical reporting
- Disease Classification: Leverages DenseNet-121 from TorchXRayVision for detecting 18 pathology classes
- X-ray Generation: Utilizes RoentGen for synthetic CXR generation
- Utilities: Includes DICOM processing, visualization tools, and custom plotting capabilities
Note the current version of MedRAX is experimentally released and does not support vision for GPT-4o and MedSAM. We will be integrating these shortly.
We introduce ChestAgentBench, a comprehensive evaluation framework with 2,500 complex medical queries across 7 categories, built from 675 expert-curated clinical cases. The benchmark evaluates complex multi-step reasoning in CXR interpretation through:
- Detection
- Classification
- Localization
- Comparison
- Relationship
- Diagnosis
- Characterization
Download the benchmark: ChestAgentBench on Hugging Face
huggingface-cli download wanglab/chestagentbench --repo-type dataset --local-dir chestagentbench
Unzip the Eurorad figures to your local MedMAX directory.
unzip chestagentbench/figures.zip
To evaluate with GPT-4o, set your OpenAI API key and run the quickstart script.
python quickstart.py
–model chatgpt-4o-latest
–temperature 0.2
–max-cases 2
–log-prefix chatgpt-4o-latest
–use-urls”>
export OPENAI_API_KEY=""
python quickstart.py
--model chatgpt-4o-latest
--temperature 0.2
--max-cases 2
--log-prefix chatgpt-4o-latest
--use-urls
- Python 3.8+
- CUDA/GPU for best performance
# Clone the repository git clone https://github.com/bowang-lab/MedRAX.git cd MedRAX # Install package pip install -e .
# Start the Gradio interface
python main.py
or if you run into permission issues
sudo -E env "PATH=$PATH" python main.py
You need to setup the model_dir inside main.py to the directory where you want to download or already have the weights of above tools from Hugging Face.
Comment out the tools that you do not have access to.
Make sure to setup your OpenAI API key in .env file!
MedRAX supports selective tool initialization, allowing you to use only the tools you need. Tools can be specified when initializing the agent (look at main.py):
selected_tools = [ "ImageVisualizerTool", "ChestXRayClassifierTool", "ChestXRaySegmentationTool", # Add or remove tools as needed ] agent, tools_dict = initialize_agent( "medrax/docs/system_prompts.txt", tools_to_use=selected_tools, model_dir="/model-weights" )
The following tools will aut
