MediSync

Inspiration
We were driven by the need for a unified and accessible platform to tackle complex medical research. Our goal was to reduce the frustration users face when sifting through scattered information sources.

What it does
MediSync centralizes medical data, analyzes user queries, and provides expert-level responses. It streamlines research by combining AI-driven insights with curated context from various sources.

How we built it
We developed a Next.js interface with an integrated LLM, leveraging secure APIs for optimized medical data retrieval. The platform uses a custom backend that processes PDFs, filters queries, and streams real-time responses to the front end.

Challenges we ran into
Balancing response accuracy and speed proved tricky, especially given the sensitivity of medical topics. Ensuring data security while handling user prompts and uploaded files was also a major concern.

Accomplishments that we're proud of
Successfully creating an AI-driven dashboard that provides clinically relevant information in a user-friendly format was a big win. We also perfected real-time streaming, ensuring that large responses arrive progressively and smoothly.

What we learned
We discovered how to fine-tune prompts for better results, especially in the specialized medical domain. We also learned best practices around session management and secure data encryption for sensitive content.

What's next for MediSync
We plan to incorporate advanced analytics and additional language model support to enrich the research experience. Further enhancements will focus on collaboration features and deeper integrations with existing medical databases.

Built With

• crypto.js
• next.js
• pinecone
• prisma