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🏥 AI Healthcare Platform

Enterprise-Grade AI Healthcare Assistant with Retrieval-Augmented Generation

Founded by: Arvind Sisodiya

FastAPI Next.js Python TypeScript Docker License

📖 Documentation • 🚀 Quick Start • 🏗️ Architecture • ✨ Features • 🛣️ Roadmap

🎯 Overview

AI Healthcare Platform is a production-ready, enterprise-grade healthcare AI system that combines:

  • 🤖 Intelligent RAG – Retrieval-Augmented Generation with semantic search, hybrid reranking, and clinical citations
  • ⚕️ Medical Intelligence – Symptom extraction, triage risk scoring, specialist recommendations
  • 🔒 Enterprise Security – Prompt injection guards, rate limiting, input validation
  • 📊 Observability – Structured logging, request tracing, latency tracking
  • 🌍 Multilingual – Automatic language detection for English, Hindi, Spanish, French

Medical Disclaimer: This system provides educational information only and is not a substitute for professional medical advice, diagnosis, or treatment.

🚀 Quick Start

Development Setup (3 minutes)

# Clone and install
git clone https://github.com/arvindsis11/Ai-Healthcare-Chatbot.git
cd Ai-Healthcare-Chatbot

# Configure environment
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY (required for LLM responses)

# Start backend
./setup.sh
./run_backend.sh        # Terminal 1: http://localhost:8000

# Start frontend (new terminal)
./run_frontend.sh       # Terminal 2: http://localhost:3000

Full Stack with Docker (1 command)

docker compose up --build

All services available at:

✨ Features

💬 Intelligent Chat

  • Symptom Analysis – Automatic symptom extraction with keyword + ML-assisted detection
  • Risk Triage – Real-time risk scoring (low 🟢 | medium 🟡 | high 🔴)
  • Source Citations – Every response includes clinical sources and evidence excerpts
  • Conversation History – Anonymous session persistence and easy restoration

🔐 Security & Safety

  • Prompt Injection Guard – Blocks jailbreak and unsafe input patterns
  • Rate Limiting – Per-IP request throttling (configurable)
  • Input Validation – Pydantic-powered request validation
  • Medical Disclaimers – Automatic safety notices on all responses

⚡ Performance & Scalability

  • Smart Caching – Redis + in-memory TTL cache for frequent queries
  • Hybrid Retrieval – Vector similarity + lexical reranking for best results
  • Document Chunking – 120-word windows with 50% overlap for precise context
  • Session Abstraction – Ready for PostgreSQL with current in-memory fallback

🛠️ Enterprise Features

  • Structured Logging – JSON logs with request IDs and latency tracking
  • Admin Dashboard – Analytics, symptom trends, user activity
  • Multilingual Support – Language detection with translation scaffolding
  • API-First Design – Clean REST endpoints for health, chat, analysis, sessions
  • Docker Ready – Full containerization with nginx reverse proxy

🏗️ Architecture

graph TB
    User["👤 User"] -->|Chat Input| FE["🌐 Next.js Frontend"]
    FE -->|HTTP/REST| API["⚡ FastAPI API"]
    API -->|Validate| Guard["🔐 Prompt Guard"]
    API -->|Detect| Lang["🌍 Language Detection"]
    API -->|Extract| Symp["⚕️ Symptom Extraction"]
    API -->|Assess| Triage["📊 Triage Service"]
    API -->|Check| Cache["⚡ Redis Cache"]
    API -->|Retrieve| RAG["🔍 RAG Pipeline"]
    RAG -->|Search| VDB["📚 ChromaDB"]
    RAG -->|Rerank| Hybrid["🔄 Hybrid Retrieval"]
    RAG -->|Generate| LLM["🤖 OpenAI LLM"]
    API -->|Persist| Session["💾 Session Store"]
    API -->|Response| FE
    FE -->|Display| User
Loading

Layered Architecture:

  • API Layer – Routing, validation, orchestration
  • Service Layer – Chat, RAG, medical intelligence, caching
  • Repository Layer – Vector DB, session persistence
  • AI Layer – Prompt guards, translation, medical analysis
  • Middleware – Request IDs, latency tracking, security headers

📊 Key Capabilities

Feature Details
RAG Pipeline Semantic search + lexical reranking + chunk-based citations
Triage System Rule-based risk detection (low/medium/high with severity scores)
Specialist Routing Automatic doctor type recommendations based on symptoms
Session Management Anonymous chat history with optional PostgreSQL backing
Caching Redis-first architecture with intelligent fallback
Logging Structured JSON logs with request IDs and latency metrics
Rate Limiting Per-IP throttling with configurable request windows
Multilingual Language detection + translation scaffolding

🛣️ Roadmap

✅ Phase 1 – AI Healthcare Assistant (Current)

  • Safe conversational healthcare guidance
  • RAG with clinical citations
  • Triage risk assessment
  • Enterprise infrastructure

📋 Phase 2 – AI Healthcare API Platform

  • Multi-tenant API management
  • OAuth2/JWT authentication
  • Usage analytics & billing

🏥 Phase 3 – AI Telemedicine Platform

  • Doctor network integration
  • Appointment scheduling
  • Longitudinal patient context

🔬 Phase 4 – AI Clinical Decision Support

  • Knowledge graph retrieval
  • Evidence-based care pathways
  • Clinician audit trails

📖 Documentation

Document Purpose
PROJECT_STATE.md Readiness assessment, tech debt, component overview
ARCHITECTURE.md Layered design, request flow, observability
SETUP.md Development & Docker deployment guides
API.md REST endpoints with request/response examples
RAG_PIPELINE.md Retrieval strategy, safety measures
SECURITY.md Security controls & recommendations
DEPLOYMENT.md Production deployment & CI/CD
PRODUCT_ROADMAP.md 4-phase product evolution
KNOWLEDGE_GRAPH.md Future knowledge graph design
ENTERPRISE_UPGRADE_SUMMARY.md Complete upgrade details
EXECUTION_CHECKLIST.md All 18 implementation steps

🧪 Testing & Validation

# Frontend
cd frontend
npm run lint      # ESLint checks
npm run build     # Production build
npm run test      # Jest unit tests

# Backend (requires dependencies)
pip install -r backend/requirements.txt
pytest tests/

# Full stack
docker compose up --build

Test Status

  • ✅ Frontend Build: Passing
  • ✅ Frontend Lint: Passing
  • ✅ Frontend Tests: Passing
  • ✅ Backend Compile: Passing
  • ✅ Docker Compose: Valid

🌟 Key Endpoints

GET  /api/v1/health               # Health check with features
POST /api/v1/chat                 # Main chat endpoint
POST /api/v1/analyze-symptoms     # Focused symptom analysis
GET  /api/v1/sessions/{id}        # Chat history retrieval

Example Request

curl -X POST http://localhost:8000/api/v1/chat \
  -H "Content-Type: application/json" \
  -d '{
    "message": "I have fever and headache",
    "symptoms": ["fever", "headache"]
  }'

Example Response

{
  "response": "Based on your symptoms...",
  "conversation_id": "abc-123",
  "symptom_analysis": {
    "symptoms": ["fever", "headache"],
    "severity_score": 5,
    "risk_level": "medium",
    "possible_conditions": ["General symptom cluster..."],
    "urgency_recommendation": "See doctor within 24-72 hours"
  },
  "recommended_specialist": "General Physician",
  "citations": [
    {
      "id": "fever.yml#chunk-1",
      "source": "fever.yml",
      "excerpt": "Fever is a common symptom..."
    }
  ],
  "sources": ["fever.yml"]
}

📁 Project Structure

📦 Ai-Healthcare-Chatbot
├── 🔧 backend/
│   ├── app/
│   │   ├── api/              # FastAPI routes
│   │   ├── services/         # Business logic
│   │   ├── repositories/     # Data access
│   │   ├── rag/              # RAG pipeline
│   │   ├── ai/               # Prompt guards, translation
│   │   ├── middleware/       # Security, tracing
│   │   ├── models/           # Pydantic schemas
│   │   └── core/             # Settings, logging, DI
│   ├── Dockerfile
│   └── requirements.txt
├── 🎨 frontend/
│   ├── src/
│   │   ├── app/              # Routes
│   │   ├── features/
│   │   │   ├── chat/         # Chat workspace
│   │   │   └── analytics/    # Admin dashboard
│   │   ├── components/       # Shared UI
│   │   ├── services/         # API clients
│   │   └── styles/           # Tailwind CSS
│   ├── Dockerfile
│   └── package.json
├── 🐳 docker-compose.yml    # Full stack orchestration
├── 📚 docs/                 # Enterprise documentation
├── ✅ tests/                # Test suites
└── 📖 README.md             # This file

💡 Enterprise Readiness

Readiness Score: 72/100

Category Score Details
Architecture 82/100 Modular layered design ✅
Safety 74/100 Prompt guards, disclaimers ✅
Observability 70/100 Structured logging & tracing ✅
Scalability 75/100 Caching, abstraction layers ✅
Testing 58/100 Core tests passing, expandable
Operations 73/100 Docker & CI/CD ready ✅

🔐 Security Features

  • ✅ Prompt Injection Protection – Pattern-based guard against adversarial input
  • ✅ Rate Limiting – Per-IP request throttling
  • ✅ Input Validation – Pydantic schemas on all endpoints
  • ✅ Security Headers – X-Frame-Options, X-Content-Type-Options, Referrer-Policy
  • ✅ CORS Configuration – Configurable allow-list
  • ✅ Medical Disclaimers – Automatic safety notices
  • 🔜 JWT Authentication – Coming in Phase 2

🤝 Contributing

We welcome contributions! Please read our Contributing Guide and Code of Conduct before getting started.

To contribute:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push branch (git push origin feature/amazing-feature)
  5. Open Pull Request

Looking for something to work on? Check the open issues or the full list of proposed improvements in GITHUB_ISSUES.md.

📜 License

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

👨‍💼 About Author

Arvind Sisodiya – Healthcare AI Architect & Full-Stack Engineer

Passionate about building enterprise-grade AI systems that make healthcare accessible and safe.

🚀 Ready to Get Started?

docker compose up --build

Questions? Check the 📖 documentation or open an issue!

Built with ❤️ for healthcare professionals and patients worldwide

About

see live demo of chatbot. follow the link

ai-healthcare-frontend.onrender.com/

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docker typescript chatbot project python3 artificial-intelligence report sqlite3 final-year-project fastapi ai-healthcare-chatbot render-deployment nextjs14 rag-pipeline

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