Lighthouse MD

Your personal AI copilot for understanding medical documents process, analyze, and chat with your medical records completely in your browser, with privacy-first local processing in multiple languages

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About The Project

💡 Inspiration

The idea for Lighthouse MD was born from a personal experience of helping my family with their medical reports in simple words. Like many people, we found medical documents overwhelming, filled with complex terminology and data that were hard to interpret. I noticed that while there were many medical document management solutions, most required uploading sensitive information to external servers, raising privacy concerns. Additionally, many existing solutions didn't offer real-time interaction or multilingual support, which is crucial for our diverse healthcare ecosystem.

🎯 What It Solves

Lighthouse MD addresses several critical challenges in medical document comprehension:

  • Privacy concerns around uploading sensitive medical data
  • Language barriers in understanding medical documents
  • The complexity of medical terminology and reports
  • The need for quick, contextual understanding of multiple medical documents
  • Access to medical document analysis without internet dependency

🛠 How I Built It

The development journey involved several key technical decisions and implementations:

Technical Integration:

  • Prompt API: Used for enabling contextual, real-time chat interactions with the content of the documents
  • Translation and Language Detection APIs: Allowed users to interact and receive responses in their preferred language, breaking language barriers.
  • Summarization API: Implemented to generate multilingual summaries of the webpage content, helping users quickly grasp the essence of the medical documents
  • Gemini Document Processing API: To extract the contents of the medical documents to consumable text

📚 What I Learned

This project was a tremendous learning experience in several areas:

  1. Technical Challenges

    • Managing memory usage with large documents
    • Implementing efficient document chunking while maintaining context
    • Extracting Bio markers from the document - using Gemini Flash API

  2. UX Challenges

    • Designing clear progress indicators for multi-stage processing
    • Balancing information density with clarity
    • Making complex features accessible to non-technical users

  3. Performance Challenges

    • Optimizing vector search for large document sets
    • To solve this I have used langchain/text_splitter to split the page content to appropriate chunks
    • For embeddings/feature extraction I have used 'Xenova/all-MiniLM-L6-v2' with Transformer.js
    • Storing the embeddings for a given page content : To solve it I have used chrome's indexedDb itself

🎯 Future Improvements

Looking ahead, there are several exciting possibilities for enhancement:

  1. Enhanced Analysis

    • Integration of medical terminology databases
    • Addition of visual analytics for lab results
    • Implementation of trend analysis across multiple documents

  2. User Experience

    • Implementation of document comparison features
    • Enhanced visualization of medical data

  3. Performance

    • Addition of document caching mechanisms
    • Optimization of vector search algorithms

  4. Accessibility

    • Addition of screen reader support
    • Implementation of voice commands
    • Enhanced keyboard navigation

This project has been a journey of learning, problem-solving, and pushing the boundaries of what's possible in browser-based applications. It's taught me valuable lessons about building privacy-focused, user-friendly solutions for complex problems, and I'm excited to continue improving it based on user feedback and emerging technologies.

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