ClinicalTrials Match - AI-Powered Trial Discovery Platform

Inspiration

Clinical trials are crucial for advancing medical treatments, but patients often struggle to find relevant studies due to complex medical jargon, scattered information, and overwhelming eligibility criteria. We were inspired by the need to bridge the gap between medical research and patient accessibility, making clinical trial discovery more democratic and user-friendly.

What it does

ClinicalTrials Match is an AI-powered platform that simplifies clinical trial discovery through intelligent search, personalized eligibility assessment, and simplified medical explanations. Users can search trials by condition, location, and phase, take an eligibility quiz for personalized matches, view trial details with both original and simplified explanations, and directly apply for trials with integrated contact features.

How we built it

We built a full-stack web application using Next.js for the frontend with TypeScript and Tailwind CSS for a modern, responsive UI. The backend uses Node.js with Express to handle API requests and integrate with the ClinicalTrials.gov API. We implemented AI-powered text simplification, eligibility scoring algorithms, and Google Maps integration for location-based features.

Challenges we ran into

One major challenge was handling the complex, inconsistent data structure from the ClinicalTrials.gov API, which required extensive null/undefined checks and data transformation. We also faced issues with the Gemini API for text simplification and had to implement a manual fallback system. Port conflicts and server management during development required careful process management.

Accomplishments that we're proud of

We successfully created a comprehensive clinical trial discovery platform that processes real-time data from ClinicalTrials.gov, implements intelligent eligibility scoring, and provides both original and simplified medical explanations. The platform features seamless Google Maps integration, conditional sponsor contact functionality, and a complete application workflow that bridges the gap between patients and clinical research.

What we learned

We learned the importance of robust error handling when working with external APIs, the value of user-centered design in healthcare applications, and how to balance technical accuracy with accessibility. We also gained experience in implementing AI features while maintaining fallback systems for reliability.

What's next for ClinicalTrials Match - AI-Powered Trial Discovery Platform

We plan to integrate with additional clinical trial databases, implement advanced AI-powered matching algorithms, add user accounts for saving preferences and application history, develop mobile applications, and integrate with healthcare provider systems for seamless patient referrals.