Health Informatics Journal, Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes

This article examines the transformative potential of artificial intelligence (AI) in healthcare through the lens of federated learning and policy reforms. Federated learning enables collaborative model development without sharing raw data, addressing critical privacy and compliance concerns. The study discusses the technical, ethical, and operational challenges faced when deploying AI in real-world healthcare settings, emphasizing the need for robust policies and infrastructure. By exploring case studies and emerging trends, the paper highlights how federated learning can accelerate AI innovation while maintaining data security, ultimately improving patient care and healthcare delivery.