How do I use Intelligent Medical Assistant?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Intelligent Medical Assistant Show me a summary of Ben's blood pressure and sleep quality for the past week." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🏥 Intelligent Medical Assistant

A comprehensive Patient-Centric Medical Intelligence System that combines MCP (Model Context Protocol) server capabilities with an intelligent web interface. This system provides natural language medical queries, patient data analysis, and professional medical insights powered by Gemini AI.

✨ Key Features

🤖 Intelligent Medical Assistant

Natural Language Processing - Ask questions like "What is Ben's sleep summary?"
Automatic Tool Routing - AI decides which patient data to access
Conversational Interface - Single input for all medical queries
Professional Medical Responses - Evidence-based information with disclaimers

👥 Patient Data Management

Real Patient Data - CSV-based patient records and biometrics
Comprehensive Tracking - Sleep patterns, vital signs, lab results, medications
Trend Analysis - Historical data analysis and insights
Multi-Patient Support - Manage multiple patient records

🔧 Dual Architecture

MCP Server - Integration with Kiro IDE and other MCP clients
Web Application - Professional web interface with authentication
RESTful API - Programmatic access to medical data and insights

📊 Available Patient Data

Ben Smith (34M) - Hypertension, Type 2 Diabetes
- 15 days of detailed sleep data (duration, quality, efficiency)
- Vital signs tracking (BP, heart rate, glucose, weight)
Sarah Jones (28F) - Asthma
Mike Wilson (45M) - High Cholesterol

🚀 Quick Start

1. Installation

# Clone and setup
git clone https://github.com/sp2learn/medical-mcp-server.git
cd medical-mcp-server

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configuration

# Copy environment template
cp .env.example .env

# Edit .env with your API keys
MEDICAL_MODEL=gemini
GOOGLE_API_KEY=your_gemini_api_key_here
DISPLAY_TIMEZONE=America/New_York

3. Run the System

Web Application:

python web_app.py
# Visit: http://localhost:8000
# Login: demo/password, doctor/secret123, admin/admin2024

MCP Server:

python server.py
# For integration with Kiro IDE or other MCP clients

🎯 Usage Examples

Natural Language Queries

The intelligent assistant understands natural language and automatically routes to appropriate tools:

🩺 "What is Ben's sleep summary for the past week?"
→ Analyzes sleep data, provides trends and insights

📊 "Show me Ben's blood pressure trends"
→ Reviews vital signs, identifies patterns

💊 "What medications is Ben taking?"
→ Lists current medications and adherence data

🔍 "What are the symptoms of diabetes?"
→ Provides general medical information

📈 "Compare Ben's glucose levels over time"
→ Analyzes lab data and trends

MCP Integration

Add to your Kiro IDE configuration (.kiro/settings/mcp.json):

{
  "mcpServers": {
    "medical-query": {
      "command": "/path/to/medical-mcp-server/venv/bin/python",
      "args": ["/path/to/medical-mcp-server/server.py"],
      "disabled": false,
      "autoApprove": [
        "medical_query",
        "symptom_checker", 
        "get_patient_sleep_pattern",
        "get_patient_vitals",
        "get_patient_labs",
        "get_medication_adherence",
        "get_patient_activity",
        "get_patient_overview"
      ]
    }
  }
}

🛠️ Available Tools

MCP Tools (8 total)

Tool	Description	Example Usage
`medical_query`	General medical Q&A	"What causes hypertension?"
`symptom_checker`	Symptom analysis	Analyze: headache, fever, fatigue
`get_patient_sleep_pattern`	Sleep data analysis	Ben's sleep for 30 days
`get_patient_vitals`	Vital signs summary	Ben's latest BP readings
`get_patient_labs`	Laboratory results	Ben's glucose trends
`get_medication_adherence`	Medication compliance	Ben's medication adherence
`get_patient_activity`	Physical activity data	Ben's activity levels
`get_patient_overview`	Complete patient summary	Ben's full medical profile

Tool Management

# List all tools and their status
python manage_tools.py list

# Show detailed tool information  
python manage_tools.py details get_patient_sleep_pattern

# Enable/disable tools
python manage_tools.py enable medical_query
python manage_tools.py disable symptom_checker

📁 Project Structure

medical-mcp-server/
├── 🤖 Core Intelligence
│   ├── intelligent_medical_assistant.py  # Natural language processing
│   ├── medical_client.py                 # AI model integration
│   └── patient_data_manager.py           # Patient data management
├── 🌐 Web Interface  
│   ├── web_app.py                        # FastAPI web application
│   ├── templates/                        # HTML templates
│   │   ├── dashboard.html                # Main interface
│   │   ├── login.html                    # Authentication
│   │   └── base.html                     # Base template
│   └── static/                           # CSS, JavaScript, assets
├── 🔧 MCP Server
│   ├── server.py                         # MCP protocol server
│   ├── tool_config.py                    # Centralized tool configuration
│   └── manage_tools.py                   # Tool management CLI
├── 📊 Patient Data
│   └── data/
│       ├── patients.csv                  # Patient demographics
│       ├── ben_sleep_data.csv            # Ben's sleep metrics
│       └── ben_vitals_data.csv           # Ben's vital signs
└── 🚀 Deployment
    ├── Dockerfile                        # Container configuration
    ├── docker-compose.yml                # Multi-service setup
    └── render.yaml                       # Render deployment config

🌐 Deployment

Local Development

source venv/bin/activate
python web_app.py
# Access: http://localhost:8000

Cloud Deployment (Render)

Push to GitHub
Connect repository to Render
Set environment variables:
- MEDICAL_MODEL=gemini
- GOOGLE_API_KEY=your_key
Deploy automatically

Docker Deployment

# Build and run with Docker Compose
docker-compose up --build

# Or build manually
docker build -t medical-mcp-server .
docker run -p 8000:8000 --env-file .env medical-mcp-server

🔐 Authentication & Security

Web Interface

Session-based authentication with secure cookies
Role-based access (demo, doctor, admin accounts)
24-hour session expiration
HTTPS ready for production deployment

Demo Accounts

Username	Password	Role	Description
`demo`	`password`	Patient	Basic demo access
`doctor`	`secret123`	Healthcare Provider	Full medical access
`admin`	`admin2024`	Administrator	System administration

📈 Patient Data Format

Patient Demographics (

patient_id,first_name,last_name,age,gender,conditions,medications,last_visit
ben_smith,Ben,Smith,34,male,"hypertension,type_2_diabetes","metformin,lisinopril",2024-01-15

Sleep Data (

date,sleep_hours,bedtime,wake_time,sleep_quality,deep_sleep_minutes,rem_sleep_minutes
2024-09-14,7.2,22:30,05:42,good,85,92

Vital Signs (

date,systolic_bp,diastolic_bp,heart_rate,temperature_f,weight_kg,glucose_mg_dl
2024-09-14,142,88,72,98.6,78.2,156

🧪 Testing

# Test MCP server functionality
python test_server.py

# Test tool configuration
python manage_tools.py list

# Test web application
curl http://localhost:8000/health

# Run comprehensive tests
./run_tests.sh

🤝 Contributing

Fork the repository
Create feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Issues: GitHub Issues
Documentation: See individual module docstrings
Examples: Check the examples/ directory

⚠️ Medical Disclaimer

This tool provides general medical information for educational and professional reference purposes only. It is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of qualified healthcare providers with any questions regarding medical conditions or treatment decisions.

Built with ❤️ for healthcare professionals and medical AI research

Intelligent Medical Assistant