MediScan — Health Report Analyzer

An AI-powered web application that analyzes medical lab report PDFs, flags abnormal values against standard reference ranges, and generates plain-English explanations for non-medical users.

Built as a portfolio project demonstrating full-stack development with LLM integration in the healthcare domain.

---

Features

• PDF Upload — Drag-and-drop or click-to-browse PDF upload
• Lab Value Extraction — Parses structured tables and raw text from lab reports
• Reference Range Flagging — Compares 26+ common lab values against standard ranges (Mayo Clinic / WHO)
• AI Explanations — Uses Claude to generate 2–3 sentence plain-English explanations for abnormal values
• Clean Dashboard — Color-coded status cards (normal / high / low / critical)
• Overall Summary — A 3–4 sentence AI-generated health snapshot

---

Tech Stack

Layer	Technology
Frontend	React 18 (Vite), plain CSS
Backend	Python 3.11+, FastAPI
PDF Parse	pdfplumber
AI / LLM	Anthropic Claude (claude-sonnet-4-20250514)
HTTP	axios (frontend), httpx (backend)
Auth	Environment variable (no database)

---

Project Structure

mediscan/
├── backend/
│   ├── main.py               # FastAPI app + /analyze endpoint
│   ├── parser.py             # PDF text extraction (pdfplumber)
│   ├── analyzer.py           # Claude API integration
│   ├── reference_ranges.py   # Standard lab reference ranges
│   ├── requirements.txt
│   └── .env.example
├── frontend/
│   ├── src/
│   │   ├── App.jsx
│   │   ├── components/
│   │   │   ├── UploadSection.jsx
│   │   │   ├── ResultsDashboard.jsx
│   │   │   ├── LabValueCard.jsx
│   │   │   └── LoadingSpinner.jsx
│   │   └── main.jsx
│   ├── index.html
│   ├── vite.config.js
│   └── package.json
├── .gitignore
└── README.md

---

Setup — Backend

Prerequisites

• Python 3.11 or higher
• An Anthropic API key

Steps

cd mediscan/backend

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate        # macOS / Linux
# venv\Scripts\activate         # Windows

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Open .env and set: ANTHROPIC_API_KEY=sk-ant-...

# Start the server
uvicorn main:app --reload --port 8000

The API will be available at http://localhost:8000.
Swagger docs: http://localhost:8000/docs

---

Setup — Frontend

Prerequisites

• Node.js 18 or higher

Steps

cd mediscan/frontend

# Install dependencies
npm install

# Start the dev server
npm run dev

The app will open at http://localhost:5173.

The frontend expects the backend to be running at http://localhost:8000.

---

Get an Anthropic API Key

1. Go to console.anthropic.com
2. Sign in or create an account
3. Navigate to API Keys → Create Key
4. Copy the key and paste it into backend/.env:

   ANTHROPIC_API_KEY=sk-ant-your-key-here
   

---

Usage

1. Start the backend (uvicorn main:app --reload --port 8000)
2. Start the frontend (npm run dev)
3. Open http://localhost:5173 in your browser
4. Upload a medical lab report PDF
5. Click Analyze Report
6. Review the results dashboard

---

Notes & Limitations

• Not a medical device. Results are AI-generated and should not replace professional medical advice.
• Text-based PDFs only. Scanned image PDFs (without OCR text) cannot be parsed in v1.
• Stateless. No data is stored — each analysis is ephemeral.
• Reference ranges are based on commonly published adult reference values and may differ from your lab's specific ranges.

---

Screenshots

(coming soon)

---

Built with

Built as a portfolio project demonstrating full-stack development with LLM integration in the healthcare domain.

• FastAPI
• Anthropic Claude
• pdfplumber
• React + Vite