Medi-Research

gg.mp4

Built with Hyperbrowser

Deep-analyze blood test results with AI-powered medical research and real-time data extraction. Upload your blood reports and get comprehensive analysis with live medical references from trusted sources like Mayo Clinic, NIH, and PubMed.

Features

• 📋 Smart File Processing - Upload PDF, HTML, or CSV blood reports
• 🔬 AI-Powered Extraction - Automatically extract and normalize test results using GPT-4o
• 🌐 Live Medical Research - Real-time crawling of trusted medical sources with Hyperbrowser
• 📊 Comprehensive Analysis - AI-generated insights with reference ranges comparison
• 📄 Evidence Documentation - Full markdown evidence with sources and processing details
• 📱 Export to PDF - Professional analysis reports for sharing with healthcare providers

Tech Stack

• Framework: Next.js 15 (App Router) with TypeScript
• AI: OpenAI GPT-4o for medical data extraction and analysis
• Web Automation: Hyperbrowser SDK for medical research crawling
• Validation: Zod schemas for type-safe data handling
• UI: Tailwind CSS with dark theme
• File Processing: PDF parsing, HTML extraction, CSV processing

Getting Started

Prerequisites

• Node.js 18+ and npm
• OpenAI API key
• Hyperbrowser API key

Get an API Key

1. Hyperbrowser API: Get your key at https://hyperbrowser.ai
2. OpenAI API: Get your key at https://platform.openai.com

Installation

# Clone the repository
git clone <repository-url>
cd mediresearch

# Install dependencies
npm install

# Set up environment variables
cp .env.example .env.local

# Add your API keys to .env.local
OPENAI_API_KEY=your_openai_api_key_here
HYPERBROWSER_API_KEY=your_hyperbrowser_api_key_here

# Run the development server
npm run dev

Open http://localhost:3000 to view the application.

API Endpoints

POST /api/fetchResults

Extracts blood test results from uploaded files using AI.

Request:

{
  "file": "base64_encoded_file_content",
  "fileType": "pdf" | "html" | "csv"
}

Response:

{
  "tests": [{
    "name": "Test Name",
    "value": "value",
    "unit": "unit",
    "refRange": "reference_range",
    "status": "normal" | "high" | "low" | "critical"
  }],
  "evidence": {
    "md": "markdown_content",
    "sources": ["source1", "source2"]
  },
  "runId": "unique_run_identifier"
}

POST /api/fetchResearch

Crawls medical sources for test-specific research using Hyperbrowser.

Request:

{
  "testName": "Blood Test Name",
  "testValue": "optional_value"
}

Response:

{
  "articles": [{
    "title": "Article Title",
    "summary": "AI-generated summary",
    "link": "source_url",
    "source": "domain_name"
  }],
  "runId": "unique_run_identifier"
}

POST /api/analyze

Generates comprehensive analysis using AI with medical research context.

Request:

{
  "results": [BloodTest[]],
  "research": [ResearchArticle[]]
}

Response:

{
  "insights": [{
    "test": "Test Name",
    "status": "normal" | "high" | "low" | "critical",
    "comparison": "value_vs_reference_explanation",
    "message": "plain_language_analysis",
    "sources": ["source1", "source2"],
    "recommendations": ["recommendation1", "recommendation2"]
  }],
  "summary": "overall_health_summary",
  "runId": "unique_run_identifier"
}

Architecture

Data Flow

1. File Upload → User uploads blood test report (PDF/HTML/CSV)
2. AI Extraction → GPT-4o extracts structured test data
3. Medical Research → Hyperbrowser crawls trusted medical sources
4. AI Analysis → GPT-4o generates insights with medical context
5. Visualization → Results displayed with status indicators and recommendations
6. Export → Professional PDF reports for healthcare providers

Key Components

• lib/types.ts - TypeScript types and Zod validation schemas
• lib/utils.ts - Utility functions for data processing and retries
• lib/pdfExport.ts - PDF generation for analysis reports
• app/api/ - API routes for data processing and analysis
• app/components/ - React components for the user interface

Medical Sources

The application crawls these trusted medical sources using Hyperbrowser:

• Mayo Clinic (mayoclinic.org)
• National Institutes of Health (nih.gov)
• PubMed (pubmed.ncbi.nlm.nih.gov)
• WebMD (webmd.com)
• MedlinePlus (medlineplus.gov)

Usage Example

// Upload a blood test PDF
const formData = new FormData();
formData.append('file', pdfFile);

// The app will automatically:
// 1. Extract test results with AI
// 2. Research each test using Hyperbrowser
// 3. Generate comprehensive analysis
// 4. Display results with status indicators
// 5. Provide downloadable PDF report

Growth Use Case

Perfect for healthcare professionals, patients, and health-conscious individuals who want to:

• Understand complex blood test results in plain language
• Research medical conditions with real-time data
• Track health metrics over time
• Share professional reports with healthcare providers
• Learn about optimal health ranges with scientific backing

Development

# Run development server
npm run dev

# Build for production
npm run build

# Start production server
npm start

# Run linting
npm run lint

Deployment

The app is ready for deployment on Vercel, Netlify, or any Node.js hosting platform. Make sure to set the required environment variables in your deployment configuration.

Important Disclaimer

This application is for educational purposes only and does not constitute medical advice. Always consult with qualified healthcare professionals for proper medical diagnosis and treatment recommendations.

Contributing

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

License

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

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Follow @hyperbrowser for updates.