Privacy-Enhanced AI for Healthcare Applications

Abstract

Privacy concerns in healthcare have prompted the development of advanced AI technologies aimed at preserving patient confidentiality while maximizing diagnostic accuracy and treatment efficacy. This paper explores current methodologies integrating privacy-enhancing techniques with AI in healthcare applications. Key focuses include anonymization protocols, federated learning frameworks, and differential privacy methods, emphasizing their role in maintaining data security without compromising the utility of AI-driven healthcare solutions. Case studies illustrate successful implementations, highlighting the evolving landscape of privacy-enhanced AI and its transformative impact on healthcare delivery.