AI Privacy and Security in Healthcare: A Systematic Literature Review
DOI:
https://doi.org/10.63144/ijt.2026.6747Keywords:
Artificial intelligence, Privacy, Security , Systematic review, TelerehabilitationAbstract
Background: Artificial intelligence is expanding into telemedicine and telerehabilitation, yet significant privacy and security concerns persist. Scope: To synthesize empirical evidence on privacy and security approaches in health care, particularly those relevant to distributed home care. Methodology: A systematic review identified 80 studies (2019 to 2025), and Latent Dirichlet Allocation (LDA) topic modeling characterized the privacy and security themes. Results: Sixty-six studies addressed privacy, only seventeen addressed security, and three studies addressed both. LDA identified four themes: patient data privacy, federated learning for medical imaging, encrypted training and secure computation, and healthcare data governance. Most studies emphasized privacy-preserving approaches, like federated learning, encryption, and differential privacy. Almost half were conducted outside healthcare environments, limiting insight into real teleclinical and telerehabilitation workflow. Conclusion: Securing healthcare AI will require a multi‑layered governance framework, broader global representation, and integration of privacy and security protections into routine clinical workflows.
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Copyright (c) 2026 Diane Dolezel, Karima Lalani, Valerie Watzlaf, Kerryn Butler-Henderson, Elise V.Z. Lambert, Mary Morton, Jamie Sand, David Gibbs , Susan Fenton
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ISSN 1945-2020 (online)
