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Review Article

A Review of AI-Wearable Technologies for Public Health Surveillance in the U.S: Challenges and Recommendations

Authors

  • Musa Olayinka Hanafi Department of Computer Science and Engineering, University of Houston, Downtown, Houston, Texas, USA https://orcid.org/0009-0000-9155-2414

    hanafiolayinka@gmail.com

  • Gbenga Adeniyi Adediran Department of Computing and Engineering, Leeds Beckett University, Leeds, UK
  • Ifedayo Akinfemisoye Department of Computer Engineering, Federal University of Technology, Akure, Nigeria
  • Sylvester Tafirenyika Department of Business Analytics, Hult International Business School, Cambridge, Massachusetts, USA https://orcid.org/0009-0000-2904-8962
  • Oluwaferanmi Bello Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA https://orcid.org/0009-0007-3521-741X
  • Confidence Nkechineyerem Chikezie Department of Health Education, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria

Abstract

Artificial intelligence (AI)–enhanced wearables generate continuous physiologic streams that can be aggregated for real-time public health surveillance. This review summarizes evidence from U.S. studies on device penetration, analytic performance, and operational value. Surveillance infrastructure has evolved from mailed case cards to cloud dashboards accepting patient-generated data; current machine-learning pipelines transform inertial, photoplethysmographic, and temperature signals into population biomarkers. Lead-time gains of two to six days over laboratory reporting have been documented for influenza, COVID-19, and heart failure admissions. However, uneven adoption, sensor bias, privacy regulation, and limited interoperability constrain scale-up. Interfacing solutions such as FHIR subscriptions and federated analytics are assessed, alongside emerging FDA guidance on AI lifecycle management. Strategic recommendations address standards consolidation, equitable subsidization of devices, algorithm auditability, and workforce training. With these measures, AI wearables could transition from consumer novelties to an integral layer of U.S. public-health intelligence, offering earlier outbreak detection, finer chronic-disease surveillance, and more precise resource allocation.

Keywords:

AI Wearable Healthcare Digital Health Surveillance Public Health Technology Remote Patient Monitoring Wearable Data Integration

Article information

Journal

Journal of Medical Science, Biology, and Chemistry

Volume (Issue)

2(2), (2025)

Pages

37-49

Published

23-07-2025

How to Cite

Hanafi, M. O., Adediran, G. A., Akinfemisoye, I., Tafirenyika, S., Bello, O., & Chikezie, C. N. (2025). A Review of AI-Wearable Technologies for Public Health Surveillance in the U.S: Challenges and Recommendations. Journal of Medical Science, Biology, and Chemistry, 2(2), 37-49. https://doi.org/10.69739/jmsbc.v2i2.739

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