AI-Powered Multimodal Health Intelligence for Neurological Disease Prevention

This project leverages AI-driven multimodal data integration to enhance neurological disease detection and public health decision-making. By analyzing environmental, genomic, neuroimaging, and epidemiological data with deep learning, it identifies biomarkers and patterns of disease progression. A real-time AI-based risk monitoring system integrates wearable sensor data, environmental factors, and clinical records to provide early health alerts. Additionally, AI-driven causal inference models explore the links between pollutant exposure and brain diseases, while a big data simulation platform predicts intervention outcomes to inform evidence-based public health policies.