Digital Transformation in Telehealth: A Systematic Review of User Trust, Privacy, and Regulatory Governance in AI-Powered Remote Monitoring Systems

Abstract

Aims: This review examines how artificial intelligence (AI)-powered remote monitoring systems (RMS) influence patient trust, privacy, and adoption in U.S. telehealth. It also explores how key regulatory agencies the Food and Drug Administration (FDA), the Department of Health and Human Services’ Office for Civil Rights (HHS/OCR), and the Centers for Medicare and Medicaid Services (CMS) operationalize AI and privacy governance in digital healthcare. Methodology: A systematic literature review following the PRISMA framework was adopted. Fifteen peer-reviewed studies were identified through searches on PubMed, Scopus, IEEE Xplore, and Web of Science. Inclusion criteria focused on AI-enabled telehealth systems addressing patient trust, data privacy, usability, and regulatory compliance. Thematic synthesis was applied to integrate technical, ethical, and policy insights. Results: Findings indicate that the FDA’s adaptive framework for Software as a Medical Device (SaMD) and Good Machine Learning Practices (GMLP) improves transparency and safety but lacks consistent post-market oversight. The HHS/OCR’s temporary relaxation of HIPAA during COVID-19 expanded access yet revealed enduring privacy and accountability gaps. CMS’s reimbursement reforms accelerated telehealth adoption, serving over 9 million Medicare beneficiaries, but sustainability concerns remain regarding fraud prevention, equity, and long-term funding. Conclusion: AI-powered RMS enhance clinical efficiency, patient monitoring, and healthcare accessibility. However, trust, privacy, and regulatory coherence remain critical for long-term adoption. Policymakers should pursue an integrated national framework aligning innovation with ethical governance, privacy protection, and equitable reimbursement to ensure that digital transformation in U.S. healthcare remains both sustainable and inclusive.