Content
AI Healthcare System
AI Healthcare System: AI-Powered Medical Insights
A production-grade healthcare platform that combines Machine Learning for disease prediction with Generative AI for intelligent medical assistance. Built with a modern microservices architecture, the system provides preliminary health assessments for multiple conditions while maintaining strict data privacy.
Tech Stack
- AI/ML: XGBoost, Gemini Pro (GenAI), Gemini Vision, FAISS (Vector DB)
- Backend: FastAPI, Python, JWT Auth
- Frontend: Streamlit
- Infrastructure: Docker, Render (Backend), Streamlit Cloud (Frontend)
Key Features
- Multi-Disease Prediction: Specialized models for Diabetes, Heart Disease, Liver Disease, Kidney Disease, and Lung Cancer.
- RAG-Based Medical Assistant: Uses Gemini Pro and FAISS to provide context-aware responses grounded in medical data, minimizing hallucinations.
- Smart Lab Report Analyzer: Utilizes Gemini Vision to extract and analyze data from PDF/Image lab reports automatically.
- Secure Architecture: Microservices design with tenant isolation and secure session management.
System Architecture
graph TD
User([User / Patient]) -->|Vitals & Queries| App[Streamlit App]
App -->|API Request| Backend[FastAPI Backend]
subgraph Engine [AI & Analytics Engine]
Backend -->|Extract Lab Reports| GeminiVision[Gemini Vision API]
Backend -->|Run Symptoms Classifier| XGBoost[XGBoost ML Models]
Backend -->|Semantic Search| FAISS[(FAISS Vector DB)]
FAISS -->|Retrieve Context| GeminiPro[Gemini Pro RAG]
end
XGBoost -->|Diagnostic Prediction| Backend
GeminiPro -->|Grounded Medical Assist| Backend
Backend -->|Response Payload| App
