AI-Driven Disease Prediction Through Federated Learning

Abstract

The convergence of artificial intelligence (AI) and neurosurgery has transformed diagnostic precision, preoperative planning, and postoperative monitoring through the integration of advanced data analytics and imaging technologies. However, the centralized storage of sensitive medical data poses significant challenges to patient privacy and data protection, particularly within neurosurgical domains that rely heavily on neuroimaging and electrophysiological datasets. This study explores the implementation of federated learning (FL) as a privacy-preserving approach for AI-driven disease prediction in neurosurgical technologies. The proposed MediPro system integrates convolutional neural networks (CNNs) with federated learning and encrypted communication protocols to analyze MRI and CT scans, offering real-time, AI-assisted preliminary diagnosis while adhering to regulatory standards such as the Personal Data Protection Act (PDPA)

Ähnliche Arbeiten