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MediVerse is a comprehensive, next-generation healthcare platform that combines traditional medical consultations with advanced artificial intelligence, machine learning, and real-time communication technologies.

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MediVerse - AI-Powered Healthcare Platform

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MediVerse

React Node.js MongoDB Express WebRTC Gemini AI License

Revolutionizing Healthcare Through AI-Powered Consultations and Intelligent Medical Analysis

MediVerse is a comprehensive, next-generation healthcare platform that combines traditional medical consultations with advanced artificial intelligence, machine learning, and real-time communication technologies. Built with modern web technologies and powered by Google Gemini AI, MediVerse creates an intelligent healthcare ecosystem that makes medical care more accessible, efficient, and personalized.

Table of Contents

🌟 Platform Overview
🤖 AI Healthcare System
💻 User Experience
🛠️ Technical Architecture
🚀 Installation & Setup
📊 Features & Capabilities
🔒 Security & Compliance
🤝 Development & Contributing

Mission & Vision

🎯 Mission

To facilitate doctors and make their work more effective and less time-consuming by providing AI-powered pre-consultation analysis, intelligent patient assessment, and seamless communication tools that enhance the quality of healthcare delivery.

🔮 Vision

To create a world where healthcare is accessible, intelligent, and personalized through the power of artificial intelligence, enabling better patient outcomes and more efficient medical practice.

Technology Stack

Core Technologies

Component Technology Version Purpose
Frontend React.js 18.2.0 Modern responsive user interface
Backend Node.js + Express + Socket.io 18.0+ / 4.18.2 RESTful API and server logic
Database MongoDB 6.0+ Document-based data storage
Authentication JWT Latest Secure user authentication
AI Integration Google Gemini API Latest Advanced AI capabilities
ML Framework Python + Scikit-learn 3.8+ Machine learning models
Video Calls WebRTC Latest Real-time communication

AI Agent Architecture

Three-Tier Intelligent Healthcare System

graph TB
    subgraph "Patient Interaction Layer"
        Patient[Patient Portal]
        Upload[Document Upload]
        Booking[Appointment Booking]
    end
    
    subgraph "AI Processing Layer"
        Agent1[Initial Assessment Agent]
        Agent2[Diagnostic Analysis Agent]
        Agent3[Care Plan Agent]
        ML[ML Prediction Models]
    end
    
    subgraph "Healthcare Provider Layer"
        Doctor[Doctor Dashboard]
        Analysis[Pre-Visit Analysis]
        Consultation[Video Consultation]
        FollowUp[Follow-up Care]
    end
    
    subgraph "Data & Intelligence"
        Gemini[Google Gemini API]
        Database[MongoDB]
        Reports[Medical Reports]
        Analytics[Healthcare Analytics]
    end
    
    Patient --> Agent1
    Upload --> Agent2
    Booking --> Agent3
    
    Agent1 --> ML
    Agent2 --> Gemini
    Agent3 --> Analytics
    
    ML --> Doctor
    Gemini --> Analysis
    Analytics --> Consultation
    
    Doctor --> FollowUp
    Analysis --> Database
    Consultation --> Reports
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Agent Specifications

🔍 Agent 1: Initial Assessment Agent

Purpose: Comprehensive patient screening and profile creation

  • Capabilities:
    • Gathers comprehensive patient information (demographics, symptoms, medical history)
    • Provides evidence-based, non-invasive health guidance
    • Creates detailed patient profiles with risk stratification
    • Determines consultation priority and specialty requirements
    • Performs initial symptom analysis and triage
  • Safety Protocol: Conservative approach with fail-safe mechanisms for emergency detection
  • Integration: Seamlessly connects with appointment booking and doctor notification systems

🧠 Agent 2: Diagnostic Analysis Agent

Purpose: Advanced medical analysis and diagnostic support

  • Capabilities:
    • Utilizes Google Gemini API with real-time medical knowledge access
    • Performs comprehensive diagnostic analysis using latest medical literature
    • Provides differential diagnosis with confidence scoring
    • Generates specialist referral recommendations with detailed reasoning
    • Creates comprehensive pre-consultation reports for healthcare providers
    • Analyzes uploaded medical reports, lab results, and imaging studies
  • Expertise: Vast medical knowledge base with continuous learning capabilities
  • Output: Structured medical insights with evidence-based recommendations

💊 Agent 3: Test Report Analyzer Agent

Purpose: Assists doctors by analyzing uploaded medical test reports and generating clinically relevant insights.

  • Capabilities:

    • Automatically scans and interprets patient-uploaded diagnostic reports (PDFs, lab results, etc.)
    • Extracts key medical parameters and highlights critical values
    • Generates concise medical summaries from the reports
    • Suggests potential diagnoses or red flags based on report data
    • Recommends relevant next steps or follow-up investigations
    • Summarizes findings for the doctor to review before the consultation

  • Integration: Works seamlessly with the report upload system; results are displayed in the doctor’s dashboard before the consultation begins

Machine Learning Models

Health Prediction System Architecture

graph LR
    subgraph "Data Input"
        PatientData[Patient Demographics]
        VitalSigns[Vital Signs]
        LabResults[Lab Results]
        Symptoms[Symptom Data]
    end
    
    subgraph "ML Models"
        DiabetesModel[Diabetes Prediction]
        HeartModel[Heart Disease Prediction]
        RiskModel[General Risk Assessment]
        OutcomeModel[Treatment Outcome Prediction]
    end
    
    subgraph "AI Integration"
        FeatureEngineering[Feature Engineering]
        ModelEnsemble[Model Ensemble]
        RiskStratification[Risk Stratification]
    end
    
    subgraph "Clinical Decision Support"
        Recommendations[Treatment Recommendations]
        Alerts[Clinical Alerts]
        Monitoring[Continuous Monitoring]
    end
    
    PatientData --> FeatureEngineering
    VitalSigns --> FeatureEngineering
    LabResults --> FeatureEngineering
    Symptoms --> FeatureEngineering
    
    FeatureEngineering --> DiabetesModel
    FeatureEngineering --> HeartModel
    FeatureEngineering --> RiskModel
    FeatureEngineering --> OutcomeModel
    
    DiabetesModel --> ModelEnsemble
    HeartModel --> ModelEnsemble
    RiskModel --> ModelEnsemble
    OutcomeModel --> ModelEnsemble
    
    ModelEnsemble --> RiskStratification
    RiskStratification --> Recommendations
    RiskStratification --> Alerts
    RiskStratification --> Monitoring
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Model Specifications

Model Accuracy Features Purpose
Diabetes Prediction 94.2% Glucose, BMI, Age, Family History Early diabetes risk detection
Heart Disease Prediction 91.8% Chest Pain, Cholesterol, BP, ECG Cardiovascular risk assessment

Platform Screenshots & Features

Patient Experience Journey

Feature Description Screenshot
Modern Landing Page Clean, professional interface showcasing platform capabilities Landing Page
Secure Account Creation HIPAA-compliant patient registration with data encryption Account Creation
Intelligent Doctor Selection AI-powered matching with specialty filtering and availability Doctor Selection
Smart Appointment Booking Real-time scheduling with automated confirmation and reminders Appointment Booking
Interactive AI Assessment Comprehensive pre-consultation analysis with intelligent questioning AI Assessment
Appointment Management Complete history tracking with follow-up scheduling Appointment History
HD Video Consultation WebRTC-powered, secure video calls with recording capabilities Video Call

Healthcare Provider Dashboard

Feature Description Screenshot
Comprehensive Doctor Dashboard Real-time patient analytics and appointment management Doctor Dashboard
AI-Generated Pre-Visit Analysis Detailed patient insights and diagnostic suggestions Pre-Visit Analysis
Appointment Management System Efficient scheduling and patient flow optimization All Appointments

Administrative Control Panel

Feature Description Screenshot
Advanced Admin Dashboard System oversight with comprehensive analytics and reporting Admin Dashboard
Healthcare Provider Management Streamlined doctor onboarding and credential verification Add Doctor

User Journey & Workflow

Patient Experience Flow

sequenceDiagram
    participant P as Patient
    participant UI as Patient Portal
    participant A1 as Assessment Agent
    participant A2 as Diagnostic Agent
    participant A3 as Care Plan Agent
    participant ML as ML Models
    participant Doc as Doctor
    participant Sys as System
    
    P->>UI: Register & Login
    UI->>A1: Initial Assessment
    A1->>P: Health Questionnaire
    P->>A1: Submit Health Data
    A1->>ML: Risk Assessment
    ML->>A1: Prediction Results
    A1->>A2: Patient Profile
    A2->>Sys: Analyze Medical History
    A2->>A3: Diagnostic Insights
    A3->>Doc: Pre-Visit Report
    P->>UI: Book Appointment
    UI->>Doc: Appointment Confirmation
    Doc->>P: Video Consultation
    Doc->>A3: Treatment Plan
    A3->>P: Follow-up Care
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Healthcare Provider Workflow

flowchart TD
    A[Doctor Login] --> B[Dashboard Overview]
    B --> C[Review AI Analysis]
    C --> D[Patient Consultation]
    D --> E[Treatment Planning]
    E --> F[Follow-up Scheduling]
    F --> G[Care Monitoring]
    
    C --> H[Review ML Predictions]
    H --> I[Validate AI Recommendations]
    I --> D
    
    D --> J[Update Patient Records]
    J --> K[Generate Reports]
    K --> L[Care Coordination]
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System Architecture

Microservices Architecture

graph TB
    subgraph "Frontend Layer"
        PatientApp[Patient Portal - React]
        DoctorApp[Doctor Dashboard - React]
        AdminApp[Admin Panel - React]
    end
    
    subgraph "API Gateway"
        Gateway[Express.js API Gateway]
        Auth[JWT Authentication]
        RateLimit[Rate Limiting]
    end
    
    subgraph "Core Services"
        UserService[User Management Service]
        AppointmentService[Appointment Service]
        VideoService[Video Call Service]
        PaymentService[Payment Service]
    end
    
    subgraph "AI/ML Services"
        AIAgent[AI Agent Service]
        MLService[ML Prediction Service]
        ReportService[Report Generation Service]
    end
    
    subgraph "External Services"
        GeminiAPI[Google Gemini API]
        WebRTC[WebRTC Servers]
        Blockchain[Blockchain Network]
    end
    
    subgraph "Data Layer"
        MongoDB[(MongoDB)]
        Redis[(Redis Cache)]
        FileStorage[(File Storage)]
    end
    
    subgraph "Message Queue"
        RabbitMQ[RabbitMQ]
        EmailService[Email Service]
        NotificationService[Notification Service]
    end
    
    PatientApp --> Gateway
    DoctorApp --> Gateway
    AdminApp --> Gateway
    
    Gateway --> Auth
    Gateway --> RateLimit
    
    Auth --> UserService
    Gateway --> AppointmentService
    Gateway --> VideoService
    Gateway --> PaymentService
    
    AppointmentService --> AIAgent
    AIAgent --> MLService
    MLService --> ReportService
    
    AIAgent --> GeminiAPI
    VideoService --> WebRTC
    PaymentService --> Blockchain
    
    UserService --> MongoDB
    AppointmentService --> MongoDB
    VideoService --> Redis
    PaymentService --> MongoDB
    
    AIAgent --> RabbitMQ
    RabbitMQ --> EmailService
    RabbitMQ --> NotificationService
    
    ReportService --> FileStorage
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Directory Structure

MediVerse/
├── frontend/                     # Patient-facing React.js Frontend
│   ├── public/                  # Static public assets
│   └── src/
│       ├── assets/             # Images, icons, styles
│       ├── components/         # Reusable components
│       ├── context/            # React context providers
│       ├── pages/              # Pages used in routing
│       └── service/            # Frontend services (e.g., API handlers)
├── admin/                        # Admin Panel (React.js)
│   ├── public/                 # Static assets
│   └── src/
│       ├── assets/             # Static assets for admin
│       ├── components/         # UI components used in admin views
│       ├── context/            # Context providers for admin
│       ├── pages/
│       │   ├── Admin/         # Admin-specific pages
│       │   └── Doctor/        # Doctor-specific pages within admin
│       └── service/            # API services for admin
├── backend/                      # Express + MongoDB Backend
│   ├── config/                 # Environment & database config
│   ├── controllers/           # API route handlers
│   ├── middlwares/            # Custom Express middleware
│   ├── models/                # Mongoose schemas
│   ├── routes/                # API route definitions
│   └── uploads/               # Uploaded files (e.g. reports)
├── disease_prediction_backend/  # ML Prediction Flask API
│   └── app/
│       ├── models/            # ML model files
│       │   └── dataset/       # Training datasets
│       └── routes/            # Flask API endpoints
├── screenshots/                 # Project screenshots for README/demo

Installation & Setup

Prerequisites

Requirement Version Purpose
Node.js ≥18.0.0 JavaScript runtime
Python ≥3.8.0 AI/ML services
MongoDB ≥6.0.0 Database

Quick Start

Run Docker

# Clone the repository
git clone https://github.com/Annonnymmousss/MediVerse.git
cd MediVerse
# In root directory
docker-compose down
docker-compose up --build

OR

1. Clone and Setup

# Clone the repository
git clone https://github.com/Annonnymmousss/MediVerse.git
cd MediVerse

# Install dependencies
npm run install:all

2. Environment Configuration

# Copy environment files
cp backend/.env.example backend/.env
cp frontend/.env.example frontend/.env

# Edit configuration files with your settings
# See Configuration section for details

3. Database Setup

# Start MongoDB
Add MongoDB url in the .env file of backend

4. ML Services Setup

# Setup Python environment
cd disease_predicton_backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install AI dependencies
pip install -r requirements.txt

# Configure Google Gemini API
export GEMINI_API_KEY=your_api_key_here

Configuration

Backend Environment Variables (.env)


Follow .env.backup and make a .env file for yourself

Frontend Environment Variables (.env)



Follow .env.backup and make a .env file for yourself`

Core Features Deep Dive

🤖 AI-Powered Healthcare Agents

Intelligent Patient Assessment

  • Multi-stage health evaluation with adaptive questioning
  • Symptom severity scoring and risk stratification
  • Medical history analysis with pattern recognition
  • Emergency case detection and immediate escalation

Advanced Diagnostic Support

  • Differential diagnosis generation with confidence scoring
  • Evidence-based treatment recommendations
  • Drug interaction checking and allergy management
  • Specialist referral optimization

📊 Machine Learning Analytics

Predictive Healthcare Models

  • Diabetes Risk Assessment: 94.2% accuracy with early detection capabilities
  • Cardiovascular Risk Analysis: 91.8% accuracy for heart disease prediction

Clinical Decision Support

  • Real-time risk alerts and notifications
  • Treatment effectiveness monitoring
  • Population health analytics
  • Quality improvement recommendations

🎥 Advanced Video Consultation

HD Video Technology

  • WebRTC-powered, browser-based video calls
  • Automatic quality adjustment based on connection
  • Screen sharing for report review
  • Session recording for medical records

Consultation Features

  • Real-time vital signs monitoring (with compatible devices)
  • Document sharing and collaborative annotation
  • Prescription generation and e-signing
  • Follow-up appointment scheduling

HIPAA Compliance Features

Administrative Safeguards

  • Security Officer appointment and responsibilities
  • Workforce training and access management
  • Information access management procedures
  • Security awareness and training programs
  • Incident response and reporting procedures

Physical Safeguards

  • Facility access controls and restrictions
  • Workstation use and access controls
  • Device and media controls
  • Secure disposal of electronic media

Technical Safeguards

  • Access control and unique user identification
  • Automatic logoff and encryption
  • Audit controls and integrity monitoring
  • Person or entity authentication

Troubleshooting

Common Issues

  1. Import Errors: Ensure all dependencies are installed
  2. File Not Found: Check dataset file paths
  3. Memory Issues: Large datasets may require more RAM
  4. Port Already in Use: Change port in main.py or kill existing process

Logs

Check console output for detailed error messages and request logs.

Contributing

  1. Fork the repository
  2. Create feature branch
  3. Make changes
  4. Add tests
  5. Submit pull request

License

This project is licensed under the MIT License.

Support

For issues and questions:

  • Check the troubleshooting section
  • Review error logs
  • Open an issue in the repository

About

MediVerse is a comprehensive, next-generation healthcare platform that combines traditional medical consultations with advanced artificial intelligence, machine learning, and real-time communication technologies.

medi-verse-xi.vercel.app

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