SehatScan AI

Intelligent Health Risk Assessment Platform

A comprehensive medical analysis platform that combines AI-powered document processing, facial health detection, and personalized risk assessment to provide actionable health insights.

Features | Tech Stack | Getting Started | Documentation | API Reference

Overview

SehatScan AI is a modern health risk assessment platform that leverages Google's Gemini Vision API to analyze medical documents and facial images. The platform extracts health metrics, detects potential conditions, and generates personalized treatment recommendations based on evidence-based medical knowledge.

Key Capabilities

Medical Report Analysis: Upload lab reports, prescriptions, or medical documents for AI-powered extraction of health metrics with status indicators (normal, low, high, critical)
Facial Health Detection: Analyze facial images for skin conditions including acne, rosacea, hyperpigmentation, dark circles, and more with precise location mapping
Risk Assessment Engine: Combine multiple data sources (reports, facial analysis, symptoms, medical history) to generate comprehensive health risk profiles
AI Health Assistant: Context-aware chatbot that answers health questions based on your analysis history

Features

Medical Document Analysis

Feature	Description
Multi-format Support	Process PDF, JPG, and PNG medical documents
Intelligent OCR	Gemini Vision API extracts text with high accuracy
Metric Extraction	Automatically identifies health metrics with reference ranges
Status Detection	Classifies values as normal, low, high, or critical
Problem Detection	Identifies health concerns with severity levels
Treatment Recommendations	Generates actionable, evidence-based treatment plans

Facial Health Analysis

Feature	Description
Face Detection	Accurate face localization with bounding boxes
Skin Condition Detection	Identifies 15+ skin conditions with confidence scores
Location Mapping	Pinpoints problem areas (forehead, cheeks, T-zone, etc.)
Severity Assessment	Classifies conditions as mild, moderate, or severe
Visual Annotations	Color-coded overlays showing detected problem areas
Personalized Treatments	Specific product and ingredient recommendations

Supported Skin Conditions

Acne and Breakouts
Rosacea
Hyperpigmentation
Dark Circles
Dry Skin
Oily Skin
Wrinkles and Fine Lines
Eczema
Psoriasis
Blackheads and Whiteheads
Age Spots and Sunspots
Melasma

Risk Assessment

Combines medical report data with facial analysis
Incorporates user demographics and symptoms
Considers medical history and current medications
Generates comprehensive markdown-formatted reports
Supports PDF export for sharing with healthcare providers

Dashboard Features

Real-time analysis statistics
Analysis history with filtering
Interactive data visualizations
Secure profile management
Notification system
Dark/Light theme support

Tech Stack

Core Framework

Technology	Version	Purpose
Next.js	16.0.8	React framework with App Router
React	19.2.3	UI component library
TypeScript	5	Type-safe development
Tailwind CSS	4	Utility-first styling

Backend Services

Technology	Version	Purpose
Prisma	6.12.0	Database ORM
PostgreSQL	-	Primary database (Supabase)
Redis	-	Caching layer (ioredis)
Clerk	6.36.5	Authentication

AI and Processing

Technology	Version	Purpose
Google Generative AI	0.24.1	Gemini Vision API
Tesseract.js	6.0.1	Fallback OCR
jsPDF	3.0.4	PDF generation
html2canvas	1.4.1	Report screenshots

UI Components

Technology	Version	Purpose
Heroicons	2.2.0	Icon system
Recharts	3.5.1	Data visualization
Headless UI	2.2.9	Accessible components
React Hot Toast	2.6.0	Notifications

Getting Started

Prerequisites

Node.js 18.0 or higher
npm or yarn package manager
PostgreSQL database (Supabase recommended)
Google Cloud account with Gemini API access
Clerk account for authentication

Installation

Clone the repository

git clone https://github.com/yourusername/sehatscan.git
cd sehatscan

Install dependencies

npm install

Configure environment variables

Create a .env file in the root directory:

# Database Configuration
DATABASE_URL="postgresql://user:password@host:port/database?pgbouncer=true"
DIRECT_URL="postgresql://user:password@host:port/database"

# Authentication (Clerk)
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY="pk_test_..."
CLERK_SECRET_KEY="sk_test_..."

# AI Service (Google Gemini)
GEMINI_API_KEY="your-gemini-api-key"
GEMINI_MODEL="gemini-2.5-flash"

# Caching (Optional)
REDIS_URL="redis://user:password@host:port"

# Supabase (Optional)
NEXT_PUBLIC_SUPABASE_URL="https://your-project.supabase.co"
NEXT_PUBLIC_SUPABASE_ANON_KEY="your-anon-key"

Initialize the database

npx prisma generate
npx prisma db push

Start the development server

npm run dev

The application will be available at http://localhost:3000

Production Build

npm run build
npm start

Project Structure

sehatscan/
├── app/                          # Next.js App Router
│   ├── api/                      # API routes
│   │   ├── analyze/
│   │   │   ├── report/           # Medical report analysis
│   │   │   ├── face/             # Facial analysis
│   │   │   └── risk/             # Risk assessment
│   │   ├── analyses/             # CRUD operations
│   │   └── chatbot/              # AI assistant
│   ├── dashboard/                # Protected routes
│   │   ├── scan-report/          # Report upload page
│   │   ├── scan-face/            # Face scan page
│   │   ├── risk-assessment/      # Risk assessment page
│   │   ├── chatbot/              # AI chatbot page
│   │   ├── history/              # Analysis history
│   │   └── profile/              # User profile
│   ├── docs/                     # Documentation pages
│   ├── components/               # Reusable components
│   └── actions/                  # Server actions
├── lib/                          # Utility libraries
│   ├── gemini.ts                 # Gemini API integration
│   ├── face-analysis-server.ts   # Face analysis logic
│   ├── clerk-session.ts          # Auth utilities
│   ├── db.ts                     # Database client
│   ├── redis.ts                  # Cache utilities
│   └── validation.ts             # Input validation
├── prisma/
│   └── schema.prisma             # Database schema
├── public/                       # Static assets
└── docs/                         # Markdown documentation

Database Schema

User Model

model User {
  id        String     @id @default(cuid())
  email     String     @unique
  password  String
  name      String?
  createdAt DateTime   @default(now())
  analyses  Analysis[]
}

Analysis Model

model Analysis {
  id               String   @id @default(cuid())
  userId           String
  type             String   // "report" | "face" | "risk"
  rawData          Json
  structuredData   Json?
  visualMetrics    Json?
  riskAssessment   String?
  problemsDetected Json?
  treatments       Json?
  createdAt        DateTime @default(now())
  user             User     @relation(fields: [userId], references: [id])

  @@index([userId])
  @@index([userId, type])
  @@index([userId, createdAt(sort: Desc)])
}

API Reference

Analysis Endpoints

Analyze Medical Report

POST /api/analyze/report
Content-Type: multipart/form-data

file: <medical-document>

Response

{
  "success": true,
  "data": {
    "metrics": [
      {
        "name": "Hemoglobin",
        "value": "14.5",
        "unit": "g/dL",
        "status": "normal",
        "reference_range": "12-16"
      }
    ],
    "problems_detected": [],
    "treatments": [],
    "summary": "Overall health status summary"
  }
}

Analyze Face Image

POST /api/analyze/face
Content-Type: multipart/form-data

file: <face-image>

Response

{
  "success": true,
  "data": {
    "face_detected": true,
    "faces_count": 1,
    "faces": [
      { "x": 100, "y": 50, "width": 200, "height": 250, "label": "Face" }
    ],
    "problem_areas": [
      { "x": 120, "y": 80, "width": 40, "height": 30, "label": "Acne" }
    ],
    "visual_metrics": {
      "redness_percentage": 15,
      "yellowness_percentage": 5,
      "overall_skin_health": "fair",
      "skin_tone_analysis": "Normal skin tone with mild redness in T-zone"
    },
    "problems_detected": [
      {
        "type": "Acne",
        "severity": "mild",
        "confidence": 0.85,
        "description": "Mild acne observed on forehead",
        "location": "Forehead"
      }
    ],
    "treatments": [
      {
        "category": "Skincare",
        "recommendation": "Use salicylic acid cleanser twice daily",
        "priority": "high",
        "timeframe": "Daily",
        "for_condition": "Acne"
      }
    ]
  }
}

Generate Risk Assessment

POST /api/analyze/risk
Content-Type: application/json

{
  "reportAnalysis": { ... },
  "faceAnalysis": { ... },
  "userData": {
    "age": 35,
    "gender": "male",
    "symptoms": ["fatigue", "headache"],
    "medicalHistory": "No significant history"
  }
}

History Endpoints

Get User Analyses

GET /api/analyses
Authorization: Bearer <token>

Get Analyses by Type

GET /api/analyses/user?type=report
Authorization: Bearer <token>

Chatbot Endpoint

POST /api/chatbot
Content-Type: application/json

{
  "message": "What do my recent test results indicate?",
  "history": []
}

Caching Strategy

SehatScan implements a multi-layer caching strategy using Redis:

Cache Type	TTL	Description
User Data	1 hour	User profile and authentication
Dashboard Stats	5 minutes	Analysis counts and metrics
Recent Analyses	2 minutes	Analysis list for history page
Individual Analysis	1 minute	Single analysis details

Security

Authentication

Clerk-based authentication with session management
Automatic user synchronization to PostgreSQL
Protected API routes with middleware validation

Data Protection

User-scoped database queries
File validation (type, size, extension)
Environment variable validation on startup
Safe error messages without sensitive data exposure

Input Validation

Server-side validation for all inputs
File size limits (10MB maximum)
Allowed file types enforcement
Sanitized error responses

Testing

Run the test suite:

# Run tests once
npm test

# Run tests in watch mode
npm run test:watch

# Run tests with UI
npm run test:ui

Scripts

Command	Description
`npm run dev`	Start development server
`npm run build`	Build for production
`npm start`	Start production server
`npm run lint`	Run ESLint
`npm test`	Run test suite
`npx prisma studio`	Open database GUI
`npx prisma db push`	Push schema changes
`npx prisma generate`	Generate Prisma client

Documentation

Comprehensive documentation is available in the /docs directory:

Document	Description
Setup Guide	Installation and configuration
Architecture	System design and patterns
Database	Schema and relationships
API Reference	Endpoint documentation
Authentication	Auth flow and security
AI Integration	Gemini API usage
Components	UI component library
Features	Feature breakdown

Performance

Optimizations

React Server Components for reduced client bundle
Redis caching reduces database queries by 80%+
Database indexing on frequently queried columns
Dynamic imports for heavy components
Image optimization with Next.js Image component
Connection pooling via Supabase

Benchmarks

Operation	Average Time
Report Analysis	3-5 seconds
Face Analysis	2-4 seconds
Risk Assessment	4-6 seconds
Dashboard Load	< 500ms (cached)

Deployment

Vercel (Recommended)

Connect your GitHub repository to Vercel
Configure environment variables in Vercel dashboard
Deploy with automatic builds on push

Docker

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build
EXPOSE 3000
CMD ["npm", "start"]

Environment Requirements

Node.js 18+
PostgreSQL 14+
Redis 6+ (optional)
512MB+ RAM

Contributing

Contributions are welcome. Please follow these guidelines:

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

Code Style

Follow TypeScript best practices
Use ESLint configuration provided
Write meaningful commit messages
Add tests for new features
Update documentation as needed

License

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

Acknowledgments

Next.js - The React Framework
Google Gemini - AI and Vision API
Clerk - Authentication Platform
Supabase - Database and Backend Services
Prisma - Database Toolkit
Tailwind CSS - CSS Framework
Vercel - Deployment Platform

Built with precision for better health insights

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Name		Name	Last commit message	Last commit date
Latest commit History 97 Commits
.vscode		.vscode
app		app
docs		docs
lib		lib
prisma		prisma
public		public
scripts		scripts
.gitignore		.gitignore
README.md		README.md
eslint.config.mjs		eslint.config.mjs
next.config.ts		next.config.ts
package.json		package.json
pnpm-lock.yaml		pnpm-lock.yaml
postcss.config.mjs		postcss.config.mjs
proxy.ts		proxy.ts
setup-new-db.md		setup-new-db.md
tailwind.config.js		tailwind.config.js
test-face-analysis.js		test-face-analysis.js
tsconfig.json		tsconfig.json
vercel.json		vercel.json
vitest.config.ts		vitest.config.ts

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SehatScan AI

Overview

Key Capabilities

Features

Medical Document Analysis

Facial Health Analysis

Supported Skin Conditions

Risk Assessment

Dashboard Features

Tech Stack

Core Framework

Backend Services

AI and Processing

UI Components

Getting Started

Prerequisites

Installation

Production Build

Project Structure

Database Schema

User Model

Analysis Model

API Reference

Analysis Endpoints

Analyze Medical Report

Analyze Face Image

Generate Risk Assessment

History Endpoints

Get User Analyses

Get Analyses by Type

Chatbot Endpoint

Caching Strategy

Security

Authentication

Data Protection

Input Validation

Testing

Scripts

Documentation

Performance

Optimizations

Benchmarks

Deployment

Vercel (Recommended)

Docker

Environment Requirements

Contributing

Code Style

License

Acknowledgments

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