MediIntel AI – Intelligent Medical Record Understanding System

Track

Creative Apps (GitHub Copilot)

Project Name

MediIntel AI – Intelligent Medical Record Understanding System

GitHub Username

Yogasubramani-003

Repository URL

https://github.com/Yogasubramani-003/MediIntel-ai

Project Description

MediIntel AI is an end-to-end healthcare intelligence platform designed to transform unstructured medical documents into structured, explainable, and actionable insights using artificial intelligence.

Healthcare data such as prescriptions, lab reports, and discharge summaries are typically unstructured and difficult to analyze at scale. MediIntel AI solves this by combining computer vision, natural language processing, and backend data engineering into a unified system.

The pipeline begins with OCR using PaddleOCR and OpenCV to extract text from scanned medical documents. The extracted text is then processed using spaCy/SciSpaCy-based NLP models to identify key medical entities such as diseases, medications, dosages, and risk factors.

All structured information is stored in a PostgreSQL database and exposed through a FastAPI backend. A real-time analytics dashboard built with Plotly visualizes patient-level insights, including medical summaries, medication tracking, and risk analysis.

Additionally, an AI-powered chat assistant enables natural language querying of medical records, allowing users to interact with healthcare data in an intuitive way.

MediIntel AI demonstrates a practical application of multi-stage AI systems combining OCR, NLP, and data visualization to improve healthcare efficiency, reduce manual workload, and support faster clinical decision-making.

The system is designed with scalability and production readiness in mind, making it adaptable for real-world healthcare environments.

Demo Video or Screenshots

https://github.com/Yogasubramani-003/MediIntel-ai/blob/main/README.md

Primary Programming Language

Python

Key Technologies Used

• Designed a full AI pipeline integrating OCR, NLP, backend APIs, and analytics visualization
• Implemented high-accuracy medical text extraction using PaddleOCR + OpenCV preprocessing
• Built structured medical entity recognition system using spaCy / SciSpaCy
• Developed scalable backend architecture using FastAPI and PostgreSQL
• Created real-time interactive dashboard using Plotly for clinical insights
• Integrated conversational AI interface for querying medical records in natural language
• Focused on modular and production-ready system design for healthcare use cases

Submission Type

Individual

Team Members

No response

Submission Requirements

• My project meets the track-specific challenge requirements
• My repository includes a comprehensive README.md with setup instructions
• My code does not contain hardcoded API keys or secrets
• I have included demo materials (video or screenshots)
• My project is my own work with proper attribution for any third-party code
• I agree to the Code of Conduct
• I have read and agree to the Disclaimer
• My submission does NOT contain any confidential, proprietary, or sensitive information
• I confirm I have the rights to submit this content and grant the necessary licenses

Quick Setup Summary

1. Clone the repository from GitHub
2. Install dependencies using pip install -r requirements.txt
3. Set up PostgreSQL database and create schema "mediintel"
4. Configure environment variables in .env file
5. Run the FastAPI backend using uvicorn backend.app.main:app --reload
6. Access API documentation at http://127.0.0.1:8000/docs
7. Upload a medical document to generate structured insights and analytics

Technical Highlights

• Designed a full AI pipeline integrating OCR, NLP, backend APIs, and analytics visualization
• Implemented high-accuracy medical text extraction using PaddleOCR + OpenCV preprocessing
• Built structured medical entity recognition system using spaCy / SciSpaCy
• Developed scalable backend architecture using FastAPI and PostgreSQL
• Created real-time interactive dashboard using Plotly for clinical insights
• Integrated conversational AI interface for querying medical records in natural language
• Focused on modular and production-ready system design for healthcare use cases

Challenges & Learnings

One of the major challenges was handling noisy and unstructured outputs from OCR, especially from scanned medical documents with varying quality. This was addressed through preprocessing techniques such as image enhancement and text normalization.

Another key challenge was structuring medical entity extraction in a way that could be reliably stored and queried from a relational database. This required careful schema design and alignment between NLP output and backend models.

Through this project, I gained strong experience in building end-to-end AI systems that integrate computer vision, natural language processing, and backend engineering into a single production-like pipeline.

Contact Information

yogalakshmisubiramaniyan@gmail.com

Country/Region

India

This is a project submission rather than a scoped contribution: it does not name a change, repository file, test, or completion criterion. Start by reviewing the linked README.md and the setup command for backend.app.main:app, then identify a specific improvement with a reproducible result before beginning work.