GenMedAI

Inspiration

With the growing importance of personalized medicine, we were inspired to build a tool that leverages AI and genetic data to make healthcare more tailored and data-driven. As students passionate about data science and biomedical applications, we envisioned an app that helps users receive better treatment insights based on their own health profile and gene markers.

What it does

Select genetic markers (e.g., rs1057910, rs9923231). Submit data to receive personalized drug effectiveness predictions. View results through a modern, responsive medical dashboard.

How we built it

Frontend: Built using React.js with Tailwind CSS for a clean medical UI. Backend: Developed using FastAPI, with Uvicorn as the ASGI server. ML Model: Trained using Kaggle datasets mapping gene markers to drug response. Authentication: Implemented basic registration and login system with frontend state management. Integration: Seamless REST API communication between frontend and backend.

Challenges we ran into

Sourcing and understanding complex genetic datasets. Mapping gene markers effectively to drug response labels. Creating a UI that is both minimal and medically intuitive. Integrating all components smoothly under time constraints.

Accomplishments that we're proud of

End-to-end working prototype of a health AI tool. Integrated ML with real-time data submission. Built a modern frontend and functional backend from scratch. Understood and applied health-related datasets effectively.

What we learned

Basics of genomics and personalized drug targeting. Full-stack ML application development. React component structuring and API integration. How to think critically about user experience in a medical setting.