MedifyAI

Table of Contents

1. Introduction
2. Scoping
   
3. Data Pipeline
   
4. Modeling
   
5. Deployment
   
6. AWS Deployment Setup
   
7. CI/CD
   
8. Monitoring
   

Demo Video

Introduction

1. Problem Statement and Overview

MedifyAI is a healthcare analytics system that enhances medical symptom analysis and patient care through AI-powered tools. The system integrates Medical Chatbots with Retrieval-Augmented Generation (RAG) framework, and automated data pipelines to provide accurate medical insights and treatment recommendations.

It uses the PMC-Patients dataset (link), which contains 167,034 anonymized patient summaries from PubMed Central (PMC).

2. Methodology

AI Model Architecture

The system is structured into three primary phases:

2.1. Medical Chatbot (HealthcarechatLLM)

• Model Used: GPT-3.5
• Purpose: Dynamic symptom collection and clinical summaries.
• Capabilities:
  • Structured symptom gathering.
  • Real-time emergency detection.
  • Clinical summary generation.
  • Bias detection for fair patient interactions.

2.2. Medical Analysis (RAG System)

• Embedding Model: sentence-transformers/all-MiniLM-L6-v2
• Generation Model: GPT-4
• Purpose: Retrieval-based medical analysis.
• Capabilities:
  • Retrieval-Augmented Generation (RAG) for case-based diagnosis.
  • Historical medical case-based recommendations.
  • Comprehensive tracking via MLflow.

2.3. Patient Report Interaction (OpenBioLLM)

• Model Used: Llama3-OpenBioLLM-70B (More Info)
• Purpose: Patients can interact with doctor reports.
• Capabilities:
  • Provides clarifications and explanations about medical findings.
  • Ensures accurate, context-aware responses.

3. Goals

1. Enhance Patient Interaction – AI-powered symptom collection chatbot.
2. Improve Diagnosis – Retrieval-based medical case insights.
3. Enable Patient Empowerment – AI-assisted medical report explanations.
4. Ensure Bias-Free AI – Robust bias detection and fairness checks.
5. Seamless MLOps Deployment – Cloud-based automation & monitoring.

The source code for our project can be found here: GitHub.

Tools Used for MLOps

Project Architecture