Faculty, Staff and Student Publications

Language

English

Publication Date

1-1-2026

Journal

International Journal of Telerehabilitation

DOI

10.63144/ijt.2065.6747

PMID

42389622

PMCID

PMC13321853

PubMedCentral® Posted Date

6-1-2026

PubMedCentral® Full Text Version

Post-print

Abstract

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.

Keywords

Artificial intelligence, Privacy, Security, Systematic review, Telerehabilitation

Published Open-Access

yes

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