dicom-mcp Model Context Protocol (MCP) Server: DICOM Data Access for AI Applications

Overview: What is dicom-mcp MCP Server?

dicom-mcp is a specialized server that facilitates querying and interacting with DICOM servers, allowing Large Language Models (LLMs) to access and analyze medical imaging metadata. This server implements the Model Context Protocol (MCP), providing tools for querying patient information, studies, series, and instances from DICOM sources such as Orthanc or dcm4chee. By adhering to standard DICOM networking protocols, dicom-mcp enables seamless integration of AI applications with medical data infrastructure.

🔧 Core Features & MCP Capabilities

dicom-mcp offers a suite of powerful functions designed for integrating with AI workflows and medical imaging systems:

1. Node Configuration Management: Allows users to define and switch between different DICOM servers, making it easy to connect to various PACS or Orthanc instances.
2. Flexible Querying Mechanisms: Provides built-in queries for selecting patient information, studies, series, and individual instances based on criteria like patient IDs, study dates, modality types, etc.
3. PDF Text Extraction: Utilizes PyPDF2 for extracting text from DICOM encapsulated PDF reports.
4. MCP Adapter Functionality: Facilitates the connection between AI applications using MCP clients (such as Claude Desktop) and various data sources.

⚙️ MCP Architecture & Protocol Implementation

This server is built on robust foundations, leveraging pynetdicom for seamless protocol interactions with DICOM-compliant systems. The implementation adheres to the Model Context Protocol's specification, ensuring compatibility across different AI applications and tools.

Workflow Integration Example: Clinician Insights with Medical Imaging

In this scenario, a radiologist can use MCP clients like Claude Desktop or Continue to access patient imaging reports stored in an Orthanc server. By integrating dicom-mcp, the client can query specific studies based on patient names, accession numbers, and more, directly from within their workflow.

Workflow Integration Example: Research Database Access

Researchers using Cursor can also benefit from integrating MCP servers like dicom-mcp to access clinical imaging data for analysis. This allows them to retrieve anonymized images and reports, enhancing the efficiency of research projects.

🚀 Getting Started with Installation

To get started with dicom-mcp:

1. Clone the Repository: git clone https://github.com/yourusername/dicom-mcp.git
2. Set Up a Virtual Environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install Dependencies: pip install -e .

💡 Key Use Cases in AI Workflows

Real-World Application Case 1: Medical Diagnosis Support

AI applications like Claude Desktop can access patient imaging data through dicom-mcp to provide real-time diagnostic support during clinical consultations, enhancing the accuracy and speed of medical decisions.

Real-World Application Case 2: Disease Detection Analytics

In research settings, Cursor can use MCP servers such as dicom-mcp to analyze vast amounts of anonymized imaging data for detecting patterns and developing predictive models for various diseases.

🔌 Integration with MCP Clients

dicom-mcp is compatible with a range of MCP clients:

• Claude Desktop: Full Support
• Continue: Full Support
• Cursor: Tools Only

Ensure your MCP client is properly configured to leverage these capabilities fully by defining the correct command and environment settings.

📊 Performance & Compatibility Matrix

For detailed compatibility information, refer to the following matrix:

Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🛠️ Advanced Configuration & Security

Advanced users can customize the server configuration by modifying the config.py file. Example MCP client setup:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Ensure your environment variables are well configured and secure.

❓ Frequently Asked Questions (FAQ)

1. Q: How do I install dicom-mcp?
   A: Clone the repository, create a virtual environment, and run pip install -e ..

2. Q: Can icom-mcp work with more clients than those mentioned in the README?
   A: Yes, it is compatible with any MCP client that follows the protocol standards.

3. Q: How secure is dicom-mcp's data handling process for clinical use cases?
   A: Security measures are implemented to protect sensitive patient data, ensuring compliance with privacy regulations like GDPR and HIPAA.

4. Q: Can I run tests on my local machine without a running Orthanc server?
   A: Yes, you can start an Orthanc Docker container as described in the README for comprehensive testing of your integration.

5. Q: What are some key use cases for clinical decision support with MCP servers like dicom-mcp?
   A: Key use cases include real-time diagnostic support during consultation and enhancing research databases with anonymized patient imaging data.

👨‍💻 Development & Contribution Guidelines

Contributions to dicom-mcp welcome! To contribute:

1. Set Up Your Development Environment: Follow the steps in the README.
2. Run Tests: Utilize pytest to ensure your changes do not break existing functionality.
3. Submit a Pull Request: Make sure all tests pass and documentation is updated.

🌐 MCP Ecosystem & Resources

For more information on Model Context Protocol (MCP) and its ecosystem, visit modelcontextprotocol.io.

MCP Protocol Flow Diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Client Compatibility Matrix:

Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

By following this comprehensive guide, developers can integrate dicom-mcp into their AI workflows, leveraging its powerful capabilities to enhance the efficiency and accuracy of medical decision support systems.