Simulator
Health Overview
Doctor Copilot
Landing Page

HealthOS — Your AI Digital Health Twin

Inspiration

Have you ever received a blood report, prescription, or medical test result and had no idea what it actually meant?

Most people collect health records throughout their lives, but those records stay scattered across PDFs, hospital portals, WhatsApp messages, and folders. Even when the information is available, understanding it is often difficult.

We wanted to build something that could take all of that information and turn it into a simple, personalized experience. That's how HealthOS was born — an AI-powered Digital Health Twin that helps users understand their health, prepare for doctor visits, and make informed decisions.

What it does

HealthOS allows users to upload:

Blood reports
Lab reports
Medical scans
Prescription images
Handwritten doctor notes

The platform analyzes the document, extracts important medical information, and builds a personalized Health Twin.

Users can then:

View their health profile
Understand important biomarkers
Track health trends
Generate doctor visit summaries
Receive evidence-backed insights
Explore future health scenarios

If users don't have a report available, they can also try built-in demo reports and experience the complete platform.

How we built it

We combined multiple technologies to create a complete health intelligence workflow.

HealthOS Workflow

Medical Document

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Document Understanding

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OCR + Vision Analysis

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Biomarker Extraction

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Clinical Rules Engine

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AI Health Twin

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Doctor Copilot

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Future Simulator

We used Gemini Vision to understand medical documents and images, while our backend processes and structures the extracted information before generating insights.

To improve reliability, we also built a rule-based clinical validation layer so the platform doesn't rely entirely on AI-generated responses.

Challenges we ran into

One of our biggest challenges was handling the huge variety of medical documents.

Some reports were clean PDFs, while others were scanned images or handwritten prescriptions. Building a system that could understand all of them consistently required multiple iterations of our document-processing pipeline.

Another challenge was balancing AI flexibility with reliability. We wanted HealthOS to provide useful insights while avoiding hallucinations, so we added document validation, confidence scoring, and rule-based health analysis.

Accomplishments that we're proud of

Built a complete Digital Health Twin experience
Added support for both reports and medical images
Created an AI-powered Doctor Copilot
Integrated evidence-based health insights
Designed a polished user experience focused on simplicity and trust
Built a scalable architecture ready for future health integrations

What we learned

This project taught us that healthcare AI isn't just about using powerful models.

Trust, validation, explainability, and user experience are equally important.

We learned how to combine AI agents, multimodal document understanding, retrieval systems, and traditional software engineering into a practical healthcare application.