Privacy Risks and Mitigation Strategies in AI-Driven Healthcare Systems: Ensuring Confidentiality in Sensitive Data

Abstract

AI-driven healthcare systems hold immense potential to revolutionize medical diagnostics, treatment plans, and patient care by leveraging vast amounts of sensitive data. However, the integration of AI in healthcare also brings significant privacy risks, including unauthorized data access, data breaches, and misuse of patient information. This paper explores the various privacy risks associated with AI-driven healthcare systems and examines effective mitigation strategies to ensure the confidentiality and integrity of sensitive health data. We analyze the implications of privacy breaches in healthcare and review technologies and regulatory frameworks designed to protect patient data. Our findings highlight the necessity of robust privacy measures, including encryption, access controls, differential privacy, and secure data sharing protocols, to safeguard sensitive information in AI-driven healthcare environments. This study aims to provide insights into the current state of privacy protection in AI healthcare systems and propose recommendations for enhancing data security and patient confidentiality.