"CURAiT

Inspiration

Healthcare data can be complex and difficult to interpret quickly. We wanted to build a platform that combines AI, healthcare analytics, and intuitive visualizations to help users understand patient health patterns and risks more easily.

What it does

CURAiT is an AI-powered healthcare intelligence platform that provides:

Patient health and risk analytics
Interactive healthcare dashboards
Risk and feature-importance visualizations
Patient-level health analysis
Anomaly and trend analysis
AI-generated healthcare insights
A conversational AI assistant for asking questions about healthcare data
CSV data import and PDF report generation

CURAiT is designed as a healthcare decision-support and analytics platform, not a replacement for medical professionals.

How we built it

CURAiT was built using React, TypeScript, Vite, Tailwind CSS, Supabase, PostgreSQL, Recharts, jsPDF, and a Gemini-powered AI assistant.

Healthcare data can be uploaded through CSV files or generated as simulated demo data. The platform processes the data, stores it in Supabase, visualizes health metrics, and provides contextual AI-powered insights through the conversational assistant

Challenges we ran into

The main challenge was bringing healthcare analytics, data visualization, and Generative AI together into one simple and interactive platform.

We also focused on responsible AI by designing the assistant as a decision-support tool rather than a system that provides medical diagnoses.

Accomplishments that we're proud of

We are proud to have built a complete interactive healthcare intelligence platform with a modern dashboard, patient-level analysis, data visualization, CSV import, AI assistance, and PDF reporting.

We also created a demo experience using simulated patient data, allowing the platform to be explored without using real patient information.

What we learned

We learned how Machine Learning concepts, healthcare analytics, data visualization, backend services, and Generative AI can work together in a single application.

We also learned that explainability, usability, context, and responsible AI are especially important when building healthcare-focused applications.

What's next for "CURAiT

We plan to add real-time Machine Learning model training, stronger SHAP-based explainability, advanced anomaly detection, data-driven patient clustering, multilingual AI support, stronger privacy controls, and more advanced healthcare data analysis.