MedAID

Inspiration

Medical practitioner have lot of challenges like -information overload, time constraints, decision fatigue to aid patient in solving their problem. Using Agentic RAG, that can help in automating those tasks and provide huge observability and clarity on the patient information.

What it does

MedAID aims to revolutionize clinical decision-making by providing a robust, AI-powered platform that combines the latest research evidence with clinical tools and patient data. By addressing the challenge of information overload and enhancing the adoption of evidence-based tools, MedAID empowers healthcare professionals to deliver higher quality care, ultimately improving patient outcomes and fostering a culture of continuous improvement in the medical field.

How we built it

We built it using llamaindex and pinecone for RAG retrieval, we used langchain framework for building agents. Streamlit for UI and FastApi backend.

Challenges we ran into

Throughout the development process, several challenges emerged:

• Integration with GenAI Tools: Ensuring compatibility and seamless integration with various generative AI tools proved complex, requiring extensive testing and adjustments.
• Multi-Agent Communication: Facilitating effective communication between multiple AI agents was challenging, necessitating the design of a robust communication protocol.
• System Latency: Addressing latency issues was critical to ensure that users received timely responses from the system, which is essential in clinical settings.

Accomplishments that we're proud of

Our team achieved several significant milestones:

• Successfully integrated multiple AI technologies into a cohesive platform that enhances clinical decision-making.
• Developed a user-friendly interface that has received positive feedback from healthcare professionals during testing phases.

What we learned

We learned about Agentic RAG and using llamaindex, pinecone and other agentic frameworks over this weekend. Tries to connect multiple agents with each other. We learned on applying Agentic RAG on health care problems.

What's next for MedAID

Looking ahead, we're planning to:

• Expand its capabilities by incorporating more specialized medical knowledge databases to support diverse clinical scenarios
• Focus on optimizing system performance to further reduce latency and improve user experience.
• Explore partnerships with healthcare institutions to conduct clinical trials, validating the effectiveness of MedAID in real-world settings.
• Continue enhancing multi-agent collaboration features to improve overall system intelligence and responsiveness

Built With

• agent
• fastapi
• langchain
• llama3
• llamaindex
• pinecone
• python