🏥 NearestDoctor - AI Healthcare Assistant

Your intelligent, AI-powered companion for smarter, faster, and safer healthcare.

▲ Click to watch the full demo

• About The Project
• Key Features
• Tech Stack
• Architecture
• Getting Started
• How It Works
• Screenshots
• Roadmap
• Achievements
• Contributing
• Team
• Acknowledgments

NearestDoctor is a full-stack, AI-powered healthcare web application that bridges the gap between patients and medical professionals. It combines machine learning, blockchain, facial recognition, and natural language processing into a seamless platform that covers the entire patient journey from first symptom to secured medical record.

💡 "Every patient deserves professional medical guidance, instantly and securely — no matter where they are."

Despite advances in digital health, patients still face three core challenges:

• 🔍 Finding the right doctor: it's hard to know which specialist fits your symptoms.
• 📆 Getting an appointment: scheduling is manual, slow, and rarely location-aware.
• 🔐 Securing medical records: records are fragmented, inaccessible, and vulnerable to breaches.

NearestDoctor was designed from the ground up to solve all three.

The platform follows a microservices-inspired MERN architecture:

• The React.js frontend communicates with the Node/Express REST API for core operations.
• AI features run as independent Flask microservices (symptom detection, face recognition, X-ray diagnosis, OCR, behavioral analysis).
• MongoDB stores application data users, appointments, blogs, shop inventory.
• Ethereum smart contracts handle patient medical records with immutability and permission-based access control.

• Node.js v16+ and npm or yarn
• MongoDB running locally or via a cloud URI (v5.9.1+)
• Python 3.8+ for AI/ML microservices
• Git

1. Clone the repository

   git clone https://github.com/ahlem-phantom/AI-HealthCare-Assistant.git
   cd AI-HealthCare-Assistant

2. Install Node.js dependencies

   npm install
   # or
   yarn install

3. Install Python dependencies (for AI services)

   pip install -r requirements.txt

4. Configure environment variables

   Create a .env file in the root directory:

   MONGO_URI=your_mongodb_connection_string
   JWT_SECRET=your_jwt_secret
   PORT=5000
   STRIPE_SECRET_KEY=your_stripe_key
   GOOGLE_MAPS_API_KEY=your_maps_key
   DIALOGFLOW_PROJECT_ID=your_dialogflow_project_id
   NANONETS_API_KEY=your_nanonets_key

5. Start the main development server

   npm run development

6. Start Flask AI microservices (in a separate terminal)

   cd py-side
   flask run

7. Open the app at http://localhost:3000 🎉

1. Select Role — choose "Patient" at the landing screen.
2. Register & Log In — via username/password or LinkedIn OAuth.
3. Describe Symptoms — chat with the Dialogflow AI bot; receive a specialist recommendation.
4. Mental Health Check — take the guided quiz and get personalized article suggestions.
5. Book an Appointment — schedule with the nearest or first-available doctor via the chatbot and Google Maps.
6. Medical Records — your records are securely stored on the blockchain; grant selective access to your chosen doctors.

1. Verify Identity — upload your national card ID; Nanonets OCR confirms authenticity in real time.
2. Register Face ID — enroll your face using the TensorFlow model for future biometric logins.
3. Choose a Plan & Pay — select a subscription tier and complete payment via Stripe.
4. Log In — use LinkedIn OAuth, username/password, or a live Face ID camera scan.
5. Use the Dashboard — manage appointments, write blogs, run X-ray lung diagnoses (COVID / TB / pneumonia), and access patient records via blockchain with granted permission.

1. Choose a Role	2. Face ID Login
3. Card ID Verification	4. Verification Success
5. Blog & Forum	6. Web Scraping Search
7. Appointments List	8. Symptom Selection
9. AI Specialist Suggestions	10. Chatbot Recommendations

• Phase 1 — Study & Prototyping

  • Problem definition, state of the art & competitive analysis
  • Feasibility study, wireframes

• Phase 2 — Design & Initial Build

  • Data model & physical/logical architecture design
  • Static frontend + first Node.js components

• Phase 3.1 — Feature Implementation (V1)

  • Full backend REST API
  • Frontend integration with all services
  • Core AI modules: symptom detection, face ID, OCR verification

• Phase 3.2 — Finalization & Deployment (V2)

  • X-ray diagnosis, mental health test, blockchain records
  • Final integration, system testing & deployment

• v3 — Coming Soon

We're actively working on the next major release:

• 🎨 Redesigned UI/UX with a modern design system
• ⚡ Migration from Node.js backend to FastAPI
• 🤖 Integration of LLM models for smarter symptom analysis and conversational AI
• 🧪 Full CI/CD pipeline for automated testing & deployment
• 📦 All libraries and dependencies upgraded to latest stable versions
• 🏗️ Scalable, cloud-ready architecture

🎖️ NearestDoctor was selected among dozens of projects to participate in the 9th Edition Ceremony of Best Engineering Projects of 2022 at Esprit School of Engineering, Tunisia.

Contributions are welcome! Here's how to get started:

1. Fork the project
2. Create your feature branch

   git checkout -b feature/AmazingFeature

3. Commit your changes

   git commit -m "Add AmazingFeature"

4. Push to the branch

   git push origin feature/AmazingFeature

5. Open a Pull Request

See open issues for ideas and known bugs. Don't forget to ⭐ star the project if you find it useful!

Built with 💕 by AlphaCoders 5 engineering students from Esprit School of Engineering, Tunisia.

Project Mentor: ameni.rommene@esprit.tn

Ahlem Laajili	Skander Turki	Syrine Zahras	Hichem Ben Zammal	Nesrine Ben Mahmoud

Important: This project was built in 2022 and several of its core libraries and dependencies are now outdated or deprecated. The codebase across all four modules react-app/, server/, py side/, and blockchain/ requires updates before it can be safely run in a modern environment.

Known areas that need attention:

• React: project uses an older setup; migration to current React + Vite recommended, update of deprecated dependencies
• Node.js / Express: dependencies likely have known CVEs; run npm audit and update
• Python / Flask: AI service dependencies (TensorFlow, OpenCV, etc.) need version pinning and updates.

A full dependency refresh is planned as part of v3. Contributions to modernize the stack are very welcome, see Contributing.

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