DEEPSYMP - Evidence-Based Diagnostic AI

Streamlit
application badge
Created by team Average Fun Enjoyer on May 01, 2024

💼In the realm of traditional diagnostics, several challenges loom large. Patients often struggle to assess symptoms accurately, leading to worsened illnesses. Healthcare accessibility remains limited, with barriers such as time and finances preventing many from seeking necessary care. The imbalanced workforce places undue pressure on doctors, potentially compromising patient care.🚀Enter DeepSymp+: An evidence-based diagnostic AI poised to revolutionize healthcare. This platform offers precise symptom evaluation, benefiting both patients and providers, and fostering a more efficient healthcare system.🛠️ We constructed DeepSymp+ using a combination of technologies, including Streamlit, LlamaIndex, MongoDB, ChatGPT, and Trulens. Leveraging OpenAI's LLM, we equipped our AI with the ability to provide medical advice based on a vast database of knowledge. Each component underwent rigorous testing and optimization using Trulens evaluation functions.🏋️‍♂️Initially, our AI faced three primary challenges: Accuracy, Context Faithfulness, and Cost Efficiency. Through diligent testing and optimization, such as enriching our medical knowledge database and optimizing our pipeline with Trulens, we successfully overcame these hurdles.🏅We take pride in developing a diagnostic AI that substantiates its evaluations with medical evidence from textbooks and research papers, contributing to healthcare improvement.🔮Our journey doesn't end here. We'll continue innovating and iterating upon DeepSymp+. Our plans include building a web application, ensuring model accuracy and safety through thorough testing, and ultimately launching the project. We remain committed to refining and enhancing our groundbreaking diagnostic AI.

Category tags:

"great work, impressive idea. continue working on it and add trulens evaluation to your code. good luck"

avatar

Walaa Nasr Elghitany

Lablab Head Judge

"Awesome job! Keep it up but consider integrating trulens evaluation into your code "

avatar

Theodoros Ampas

Technical Mentor

"Always love further and deeper model architecture implementations for RAG into the medical sector. There could have been a little more originality in the solution but I like everything else"

avatar

Shebagi Mitra

Technical Mentor