Security and Privacy in AI Healthcare Systems: A Systematic Literature Review on Aligning Innovation with HIPAA Regulations and Hospital Cybersecurity
Year: 2025
Type: review
Abstract: Background: Artificial intelligence (AI) is revolutionizing healthcare delivery through innovations in diagnosis, personalized treatment, and operational efficiency. However, the growing integration of AI systems raises significant concerns about the security and privacy of patient data, requiring careful alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and hospital cybersecurity practices. Objective: This systematic literature review aims to analyze and synthesize current scientific evidence on security and privacy challenges in implementing AI in healthcare, focusing on aligning technological innovations with privacy regulations and hospital cybersecurity frameworks. Methods: A comprehensive systematic search was conducted in PubMed, IEEE Xplore, Scopus, and Google Scholar databases for studies published between 2010 and 2024, following PRISMA guidelines. Studies addressing security, privacy, ethics, and regulatory compliance of AI systems in healthcare contexts were included. Quality assessment was performed using the AMSTAR-2 tool for systematic reviews and the Newcastle-Ottawa Scale for observational studies. Results: Of 2,847 initially identified records, 47 studies met inclusion criteria after full-text screening. The analysis revealed persistent challenges, including security vulnerabilities (reported in 89% of studies), data re-identification risks (76% of studies), and gaps in regulatory frameworks regarding HIPAA coverage for AI developers (68% of studies). Healthcare professionals' hesitation to adopt AI technologies due to concerns about transparency and data security was a recurring theme (>60% adoption resistance). Emerging technologies including Explainable AI (XAI), federated learning, and differential privacy were identified as promising solutions for risk mitigation. Conclusions: Safe and ethical implementation of AI in healthcare requires a multifaceted approach that integrates technological innovations, adaptive regulatory frameworks, and robust cybersecurity protocols. Interdisciplinary collaboration and transparency are fundamental to building trust and ensuring that AI benefits can be realized responsibly, protecting patient privacy and security.
Author: Caetano, Rafael Magalhães
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7085864565
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