AI Medical Report Analyzer

A web-based application that helps users understand medical reports by extracting key information and generating a simple, patient-friendly summary using modern NLP techniques.

⚠️ Important: This tool is strictly for informational purposes. It does not provide medical advice, diagnosis, or treatment recommendations.

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🚀 Live Demo

🔗 [Add your Streamlit Cloud URL here]

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🧠 What this project does

Given a raw medical report (free-text), the system:

• Extracts key entities (e.g., dates, drugs, procedures, organizations)
• Generates a readable summary using a transformer-based model
• Clearly communicates limitations and safety boundaries
• Presents results through a simple, accessible web interface

The focus is on document understanding, not medical decision-making.

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🏗️ Architecture (High Level)

• Frontend / UI

  • Streamlit (Python)

• NLP & ML

  • spaCy (entity extraction)
  • Transformer-based summarization (BART)

• Model Handling

  • Cached models for performance
  • Dynamic input-length handling

• Ethics & Safety

  • Explicit non-diagnostic scope
  • Prominent disclaimers

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🛠️ Tech Stack

• Python
• Streamlit
• spaCy
• Hugging Face Transformers
• PyTorch

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📦 Project Structure

ai-medical-report-analyzer/
│
├── app.py              # Streamlit application
├── requirements.txt    # Project dependencies
├── README.md
├── notebooks/          # Experiments & exploration
├── data/               # Dataset (optional)
└── .gitignore

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▶️ Run Locally

1. Clone the repository

git clone https://github.com/<your-username>/ai-medical-report-analyzer.git
cd ai-medical-report-analyzer

2. Create & activate virtual environment

python -m venv venv
source venv/bin/activate   # macOS / Linux

3. Install dependencies

pip install -r requirements.txt

4. Run the app

streamlit run app.py

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⚠️ Disclaimer

This application is intended to assist with understanding medical text only. It does not replace professional medical consultation, diagnosis, or treatment.

Always consult a qualified healthcare professional for medical concerns.

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📌 Notes on Responsible AI

• No medical inference or diagnosis is performed
• Output is constrained and explainable
• Long inputs are safely handled to avoid hallucinations
• The system prioritizes clarity over speculation

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📄 License

This project is released for educational and community use.