The Unified Business Problem: Healthcare professionals are drowning in two distinct but related floods of data: a flood of predictive data (medical images, patient metrics) and a flood of unstructured knowledge (clinical guidelines, research papers, drug databases). A system that only solves one half of the problem is incomplete. This platform provides a single, unified "co-pilot" that delivers both AI-driven diagnostics from patient data and trustworthy, evidence-based answers from the world's medical knowledge.

This project combines Deep Learning for Diagnostics with Generative AI for Knowledge (RAG) into one cohesive, enterprise-grade system.

Module 1: The Data Engineering & Cloud Foundation (The Unified & Secure Data Core)

A deep-dive framework in building a secure, multi-modal data architecture using Infrastructure as Code (IaC) with Terraform. The platform runs on a HIPAA-compliant Azure environment featuring:

• Azure Blob Storage: For raw medical images and unstructured knowledge documents.
• PostgreSQL (with PostGIS): For structured patient appointment and diagnostic result data.
• Azure AI Search: For the Generative AI's vector index.
• Dual Ingestion Pipelines: Parallel Python pipelines for processing medical images (Predictive) and chunking/embedding medical documents using LangChain (Generative).

Module 2: The AI & ML Core (The Hybrid Brain)

The platform features two interconnected "brains":

• The Predictive Brain: Uses Computer Vision (ResNet50/PyTorch) to classify medical images and Classic ML (LightGBM) to predict patient no-show risks. It includes Explainable AI (Grad-CAM) to visualize model decisions.
• The Generative Brain: A Retrieval-Augmented Generation (RAG) pipeline using Azure AI Search and GPT-4 to provide safe, evidence-based answers with source citations.
• Strategic Integration: The output of the Predictive Brain becomes context for the Generative Brain, allowing clinicians to ask questions about detected conditions seamlessly.

Module 3: The Application Layer (The Clinician's Co-Pilot)

The unified interface where the magic becomes tangible:

• FastAPI Backend: A robust single API serving all AI capabilities.
• Streamlit Dashboard: A "Clinical Intelligence Co-Pilot" interface allowing clinicians to upload images, view diagnostics, and chat with the AI about treatment guidelines in real-time.
• Containerization: Fully packaged in Docker containers for consistent deployment.

Module 4: CI/CD & MLOps (Managing a Hybrid AI Platform)

Demonstrating the management of a complex, real-world AI system:

• GitHub Actions: Standardized version control and unified CI/CD pipelines for automated testing and deployment to Azure.
• Advanced MLOps: Pipelines for managing GPU-intensive training and automated re-indexing of the RAG knowledge base when new guidelines are published.