A healthcare-focused project aimed to empower patients with better understanding of their lab reports and medical conditions through a Retrieval-Augmented Generation (RAG) powered application. Due to privacy considerations, the healthcare institution behind this initiative remains confidential. Their vision was to create a solution that could deliver easy-to-understand, accurate, and personalized health education to patients directly from their medical data.
The primary challenge was to design a system that could interpret complex lab data and medical terminology, then explain it in clear, layman-friendly language — along with relevant context and potential treatment options. The app also needed to support diverse inputs like PDFs and provide reliable, medically-informed outputs based on trusted real-world data.
We built a robust RAG (Retrieval-Augmented Generation) app with a user-friendly frontend using Streamlit and a powerful backend leveraging LangChain, FAISS, and open-source LLMs. The system integrates PDF upload capabilities, extracts lab data automatically, and retrieves precise, patient-centric explanations from a vectorized knowledge base trained on curated medical resources.
The result is a functional, intelligent application that bridges the gap between medical jargon and patient comprehension. Patients can now upload lab reports and receive tailored, trustworthy interpretations — including what specific test values mean, associated health implications, and general treatment guidance — all in real time. This solution enhances patient education, reduces confusion around medical results, and supports better patient-doctor communication.
