🩺 Medical Report Analyzer

Intelligent, Structured Clinical Summaries Powered by Google Gemini

An end-to-end web application that transforms complex, dense medical lab reports and clinical PDFs into clean, structured, and plain-language medical intelligence using Google's state-of-the-art Gemini 3.6 Flash model and client-side privacy-first PDF parsing.

Explore Demo • Key Features • Quick Start • Architecture • Deployment

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Warning

Clinical Disclaimer: This application is strictly an educational and informational tool designed to assist comprehension of medical documentation. It does not provide medical diagnoses, treatment plans, or clinical judgements. Always consult a qualified healthcare provider for medical advice.

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🎬 Demo

medical_report_analyzer_demo.mp4

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✨ Key Features

🔒 Privacy-First PDF Parsing Uses pdf.js directly in the client browser to extract selectable text up to 20MB. Raw PDF binaries never touch server storage, protecting sensitive patient records.	⚡ Google Gemini 3.6 Flash Harnesses Google's latest multimodal LLM with ultra-fast inference and high clinical reasoning accuracy via the official google-genai Python SDK.
📊 Strict JSON Schema Enforcement Guarantees predictable, strongly-typed JSON output matching clinical taxonomy—eliminating hallucinations, parsing errors, and messy Markdown formatting.	🧪 Granular Lab Findings Breakdown Automatically isolates laboratory markers, reference ranges, observed values, status flags (normal, abnormal, critical), and plain-language definitions.
🚨 Urgent Alerts & Action Items Highlights high-priority clinical flags and generates targeted discussion questions for patients to bring to their next clinician appointment.	☁️ Production-Ready Serverless Pre-configured for Vercel Serverless Functions with tailored 60-second timeouts and automatic fallback handling.

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🏗️ Architecture & Data Flow

graph LR
    A[📄 User Uploads PDF] --> B[🖥️ Browser Client: PDF.js]
    B -->|Extracts Text| C[🌐 Client Web UI]
    C -->|POST /api/analyze| D[⚙️ Flask Backend on Vercel]
    D -->|Strict Clinical Schema| E[✨ Google Gemini 3.6 Flash]
    E -->|Structured JSON| D
    D -->|Validated Response| C
    C -->|Renders Visual Dashboard| F[📊 Structured Clinical Cards]

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🛠️ Tech Stack

• AI Model: Google Gemini 3.6 Flash via google-genai SDK
• Backend: Python 3.10+, Flask 3.1, Flask-CORS, python-dotenv
• Frontend: Modern Vanilla JS, Semantic HTML5, CSS3 Custom Properties (Dark/Light aesthetic)
• PDF Engine: PDF.js (Mozilla) client-side extraction
• Hosting: Vercel (Serverless Python runtime with maxDuration: 60s)

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📁 Repository Structure

Medical-Report-Analyzer/
├── api/
│   └── index.py            # Flask API routes, Gemini SDK client & schema definition
├── templates/
│   └── index.html          # Responsive client application, UI & PDF.js extraction
├── requirements.txt        # Frozen dependencies (google-genai, flask, etc.)
├── vercel.json             # Vercel Serverless Function & routing configuration
├── .env.example            # Sample environment variables template
├── .gitignore              # Ignores .env, virtual environments & runtime logs
├── LICENSE                 # MIT License
└── README.md               # Project documentation

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🚀 Quick Start

Prerequisites

• Python 3.10+ installed
• A free Google AI Studio API Key (Get one here)

1. Clone the Repository

git clone https://github.com/Poovarasan46/Medical-Report-Analyzer.git
cd Medical-Report-Analyzer

2. Set Up a Virtual Environment

# Windows (PowerShell)
python -m venv .venv
.venv\Scripts\Activate.ps1

# macOS / Linux
python3 -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the project root:

GEMINI_API_KEY="your-gemini-api-key-here"
GEMINI_MODEL="gemini-3.6-flash"

5. Run the Server

python api/index.py

Access the application in your browser: 👉 http://127.0.0.1:5000

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⚙️ Configuration Reference

The application can be configured via environment variables:

Variable	Required	Default	Description
GEMINI_API_KEY	Yes	—	API key generated from Google AI Studio
GEMINI_MODEL	No	gemini-3.6-flash	The Gemini model identifier
MAX_REPORT_CHARS	No	45000	Safety limit for character length sent to Gemini
GEMINI_MAX_COMPLETION_TOKENS	No	8192	Maximum token limit for structured output response

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☁️ Cloud Deployment (Vercel)

This repository is pre-configured with vercel.json for one-click deployment to Vercel Serverless:

1. Push your code to GitHub.
2. Navigate to Vercel and click Add New > Project > Import Git Repository.
3. Under Settings > Environment Variables, add:
   • GEMINI_API_KEY: (your Google Gemini API key)
4. Click Deploy.

Tip

The included vercel.json already sets maxDuration: 60, ensuring Vercel gives Gemini sufficient execution time to analyze long medical records without timing out.

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📋 Structured Output Schema

The AI responds with deterministic JSON adhering to this clinical structure:

{
  "summary": {
    "report_type": "Orthopedic Surgical Consultation",
    "overall_assessment": "Post-operative evaluation with stable progression.",
    "confidence": "high",
    "data_quality": "good"
  },
  "patient": { "name": "...", "age": "...", "gender": "..." },
  "provider": { "facility": "...", "clinician": "..." },
  "key_findings": [
    {
      "test_name": "Hemoglobin",
      "value": "13.8",
      "unit": "g/dL",
      "reference_range": "12.0 - 16.0",
      "status": "normal",
      "plain_language_meaning": "Healthy red blood cell count with no signs of anemia."
    }
  ],
  "possible_concerns": [...],
  "recommendations": [...],
  "urgent_flags": [...],
  "follow_up_questions": [...]
}

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🤝 Contributing

Contributions are welcome! If you have suggestions or improvements:

1. Fork the Project
2. Create a Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit Changes (git commit -m 'Add AmazingFeature')
4. Push to Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

Distributed under the MIT License. See LICENSE for more information.

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_{Built with ❤️ using Google Gemini & Flask}