Medical Diagnosis AI

An AI-powered patient symptom analysis and medical literature research assistant. This repository provides a dual interface: a Model Context Protocol (MCP) server for seamless integration with AI agents (like Claude Desktop or Cursor) and a FastAPI REST API endpoint.

It automatically extracts symptoms from natural language text, uses OpenAI's GPT-4 to suggest potential diagnoses/cures, retrieves relevant medical literature directly from PubMed (via NCBI Entrez APIs), and summarizes the scientific abstracts.

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Features

• Symptom Extraction: Uses regex-based text processing to extract symptoms (headache, fever, nausea, fatigue, pain) from patient descriptions.
• AI-Driven Diagnosis: Leverages OpenAI GPT-4 to analyze symptoms, suggest potential diagnoses, and recommend management strategies/cures.
• PubMed Integration: Queries NCBI Entrez APIs to fetch titles, authors, dates, URLs, and abstracts of the latest matching scientific literature.
• Scientific Abstract Summarization: Utilizes GPT-4 to compile a structured summary of the retrieved PubMed publications.
• Model Context Protocol (MCP) Support: Built with FastMCP for quick integration with modern LLM clients.
• FastAPI Endpoint: Exposes a POST endpoint /diagnosis for standard HTTP client integration.

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Project Structure

medical_diagnosis_ai_mcp/
├── tools/
│   ├── diagnosis_tools.py     # OpenAI GPT-4 interface for suggesting diagnoses/cures
│   ├── symptom_extractor.py   # Regex-based symptom extractor
│   ├── pubmed_fetcher.py      # NCBI Entrez utility for searching and fetching articles
│   └── summarizer.py          # OpenAI GPT-4 interface for summarizing abstracts
├── mcp_tools.py               # FastMCP server definition & entrypoint
├── fastapi_app.py             # FastAPI server & route handlers
├── pyproject.toml             # Project configuration and basic package dependencies
├── uv.lock                    # Locked dependencies
└── .env                       # Environment variables (OpenAI keys, base URLs)

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Prerequisites

• Python: 3.12 or higher
• OpenAI API Key (or OpenRouter/compatible LLM provider API Key)

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Installation & Setup

1. Clone the repository:

   git clone <your-repo-url>
   cd medical_diagnosis_ai_mcp
   

2. Set up environment variables: Create a .env file in the root directory (or edit the existing one) with your credentials:

   OPENAI_API_KEY="your-api-key-here"
   OPENAI_BASE_URL="https://api.openai.com/v1" # Or OpenRouter/alternative endpoint
   

3. Install dependencies: We recommend using uv or standard pip:

   # Using uv
   uv sync
   
   # Or using pip
   pip install mcp[cli] fastapi uvicorn openai requests beautifulsoup4 lxml python-dotenv
   

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How to Run

1. Model Context Protocol (MCP) Server

To run the server in development/dev mode or standard mode:

• Run directly:

  python mcp_tools.py
  

• Run with MCP CLI (Dev Mode):

  mcp dev mcp_tools.py
  

2. FastAPI REST Server

To start the REST API server with live reloading:

uvicorn fastapi_app:app --reload

Once running, the interactive Swagger documentation will be available at http://127.0.0.1:8000/docs.

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Usage Examples

FastAPI API Call

Send a POST request to /diagnosis to run the complete diagnostic analysis:

Request:

curl -X POST "http://127.0.0.1:8000/diagnosis" \
     -H "Content-Type: application/json" \
     -d '{"description": "The patient complains of severe headache, recurring fever, and extreme fatigue."}'

Response Schema:

{
  "symptom": [
    "headache",
    "fever",
    "fatigue"
  ],
  "diabnosis": "Based on the symptoms of severe headache, recurring fever, and extreme fatigue, possible diagnoses include...\n\nSuggested cures/treatment...",
  "pubmed_summary": "Unified summary of PubMed studies concerning headache, fever, and fatigue..."
}

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Integrating with Claude Desktop

To expose the tool in Claude Desktop, add the following configuration to your claude_desktop_config.json:

On Windows: File path: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "medical-diagnosis-ai-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--path",
        "C:\\path\\to\\your\\medical_diagnosis_ai_mcp",
        "mcp_tools.py"
      ],
      "env": {
        "OPENAI_API_KEY": "your-api-key-here",
        "OPENAI_BASE_URL": "https://api.openai.com/v1"
      }
    }
  }
}

(Replace C:\\path\\to\\your\\medical_diagnosis_ai_mcp with the absolute path to your project folder.)