ENSURING DATA PRIVACY AND SECURITY IN HEALTHCARE COMPUTER VISION AND AI APPLICATIONS: INVESTIGATING TECHNIQUES FOR ANONYMIZATION, ENCRYPTION, AND FEDERATED LEARNING

Abstract

The integration of computer vision and artificial intelligence (AI) technologies in healthcare has the potential to revolutionize patient care, clinical decision-making, and medical research. However, the handling of sensitive medical data raises significant concerns regarding data privacy and security. This research article explores techniques for ensuring data privacy and security in healthcare computer vision and AI applications, focusing on anonymization, encryption, and federated learning. By examining current research, best practices, and future directions, we aim to highlight the importance of robust data protection measures and their impact on the responsible development and deployment of AI-driven healthcare solutions. The article also discusses the challenges and considerations associated with implementing these techniques, including regulatory compliance, data utility, and computational overhead.