Eagle - AI Health Solutions

👩🏾‍⚕️ Eagle - AI Health Solution is a cutting-edge data infrastructure designed for the medical sector, enabling real-time data movement and delivering AI-driven insights and recommendations.

Comment 1

👉🏾Full Documentation

Inspiration

Eagle - AI Health Solution is a cutting-edge data infrastructure designed for the medical sector, enabling real-time data movement and delivering AI-driven insights and recommendations to revolutionize healthcare decision-making.

Eagle AI Health Solution is not only designed to help medical professionals make quick and accurate decisions but also serves as a robust data infrastructure for the healthcare industry.

What it does

👩🏾‍⚕️ Eagle - AI Health Solution is broken down into 2 major components:

Part 1: Application Data Entry

Medical data is entered into the Snowflake database via a dedicated application. This system allows medical personnel to perform CRUD operations (Create, Read, Update, and Delete) by efficiently receiving and managing patient information.

Part 2: Q&A LLM Deployment

The Mistral AI model is integrated with the Snowflake database to provide actionable insights from the data. This integration supports informed decision-making within the organization. It provides medical professionals the ability to interact with the database and get needed informations.

How we built it

Our solution was developed using a combination of powerful data tools. We utilized the Snowflake Data Warehouse for robust data storage and management, integrated the Mistral AI API to perform Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs), and deployed the solution on Streamlit. This deployment enables other medical professionals to seamlessly interact with the system.

Challenges we ran into

  • Rate Limiting: One of the major challenges we encountered during development was rate limiting. This caused delays in progress, requiring us to pause multiple tasks and reconsider our approach to ensure efficient execution.

  • Accuracy Issues: The model initially failed to respond accurately to specific questions. This necessitated multiple adjustments and fine-tuning to improve its performance and reliability.

Accomplishments that we're proud of

We successfully integrated Mistral AI with Snowflake to implement a Retrieval-Augmented Generation (RAG) model solution. This enabled us to extract valuable insights from the data and develop a user-friendly graphical interface using Streamlit, allowing seamless interaction with the data for end-users.

What we learned

Throughout this project, we gained invaluable insights into leveraging modern data tools and AI technologies to solve real-world challenges. We learned:

  • Integrating Advanced Technologies: Successfully combining Snowflake for data management, Mistral AI for Retrieval-Augmented Generation (RAG), and Streamlit for user interaction taught us how to create a robust and interactive solution.

  • Overcoming Development Obstacles: Addressing challenges like rate limiting and accuracy issues emphasized the importance of adaptability, fine-tuning models, and optimizing workflows to maintain project momentum.

  • Building User-Centric Solutions: Designing an intuitive GUI using Streamlit reinforced the importance of accessibility and usability for medical professionals interacting with the system.

What's next for Eagle - AI Health Solutions

Our next step is to make 👩🏾‍⚕️ Eagle - AI Health Solutions production-ready. This involves integrating the system into a containerized service and orchestrating the entire process using Kubernetes. These enhancements will ensure scalability, reliability, and seamless deployment in real-world environments.

Built With

  • mistral-ai
  • python
  • snowflake
  • streamlit
Share this project:

Updates