AI Privacy and Security in Healthcare: A Systematic Literature Review

Artificial intelligence is expanding into telemedicine and telerehabilitation, yet significant privacy and security concerns persist.

To synthesize empirical evidence on privacy and security approaches in health care, particularly those relevant to distributed home care.

A systematic review identified 80 studies (2019 to 2025), and Latent Dirichlet Allocation (LDA) topic modeling characterized the privacy and security themes.

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.

Securing healthcare AI will require a multi-layered governance framework, broader global representation, and integration of privacy and security protections into routine clinical workflows.