Artificial Intelligence and Machine Learning in Healthcare: Developing Privacy-Preserving Frameworks

Abstract

Objective: This study aims to investigate the revolutionary potential of Artificial Intelligence (AI) in healthcare, while addressing the pertinent issue of patient data privacy. It addresses the legal, ethical, and technological aspects of integrating AI into healthcare systems, focusing on safeguarding patient confidentiality and ensuring adherence with data protection regulations. Methods: The study involves a comprehensive review of existing literature on AI applications in healthcare, data privacy techniques, extant and relevant legal frameworks. It investigates various privacy-preserving methods, including Differential Privacy (DP), Federated Learning (FL), Homomorphic Encryption (HE), Blockchain Technology, and Secure Multi-Party Computation (SMPC). In addition, it proposes a novel AI framework called Safe Sync-Aegis AI (S²AI), designed to integrate these techniques for an optimally enhanced privacy protection. Findings: The study finds that AI has the potential to revolutionize healthcare by improving diagnostics, patient care and treatment planning. However, it also identifies important privacy risks associated with the use of sensitive patient data in AI applications. The analysis of several privacy-preserving techniques indicates that a multi-faceted approach is essential to effectively mitigate these risks. Summary: The integration of AI in healthcare presents both obstacles and opportunities. While AI offers promising solutions for improving healthcare services, it is vital to prioritize data security and patient privacy. This research earmarks the importance of adopting a comprehensive strategy that incorporates robust privacy-preserving techniques, strict data protection protocols, and ethical considerations to ensure the responsible and beneficial use of AI in healthcare.

Keywords: Artificial Intelligence in Healthcare, Machine learning in Healthcare, AI for Privacy Protection, Privacy Law, HIPAA, GDPR, Healthcare, Healthcare Information, Sensitive Data