Data Privacy and Security in AI-Enhanced Healthcare Systems

Abstract

The integration of Artificial Intelligence (AI) in healthcare has transformed diagnostic precision, treatment personalization, and operational efficiency. However, this paradigm shift raises critical challenges in ensuring data privacy and security. AI-enhanced healthcare systems depend on large-scale, sensitive datasets such as electronic health records (EHRs), genomic sequences, and real-time monitoring data. Safeguarding this information against unauthorized access, adversarial attacks, and misuse is crucial for maintaining trust, compliance with regulations, and patient safety. This paper examines the multidimensional aspects of data privacy and security in AI-driven healthcare, focusing on technical vulnerabilities, ethical dilemmas, and regulatory frameworks. It explores cutting-edge approaches such as privacy-preserving machine learning, federated learning, blockchain integration, and zero-trust architectures, highlighting their potential to secure data flows without hindering innovation. By bridging the gap between technological solutions and policy frameworks, this study provides insights into building resilient, transparent, and ethically aligned AI systems for healthcare.