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Vol. 2 No. 2 (2025)

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January 12, 2026
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December 31, 2025

Copyright (c) 2025 Mesfer Zaid Hathal Alkhamis, Fahad Mohmmed Al Thafir, Mohammed Abdullah Alskhabrah, Mohammed Razqan Matar Almutairi, Mohammed Fahad Bin Zayid, Sultan Ghusayn Aldawsari, Mohammed Mahdi Mufarrah Al-Kubra, Mohammed Abdullah Al Suliman, Mishal Rashid Al Juma, Nasser Saeed Alwuhayyid, Zakaria Mohammed Al Muhaymid, Alanoud Abdullah Alhulayyil, Hessa Trad Alonzay

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Artificial Intelligence–Enabled Wearable Sensors for Continuous Health Monitoring-An Updated Review for Biomedical Engineering

https://doi.org/10.64483/202522489
Mesfer Zaid Hathal Alkhamis
Al-Aflaj Hospital,Ministry of Health, Saudi Arabia
Fahad Mohmmed Al Thafir
Al-Aflaj Hospital,Ministry of Health , Saudi Arabia
Mohammed Abdullah Alskhabrah
Al-Aflaj Hospital,Ministry of Health , Saudi Arabia
Mohammed Razqan Matar Almutairi
King Saud University, Saudi Arabia
Mohammed Fahad Bin Zayid
King Saud University, Saudi Arabia
Sultan Ghusayn Aldawsari
Al-Aflaj Hospital,Ministry of Health , Saudi Arabia
Mohammed Mahdi Mufarrah Al-Kubra
Al-Aflaj Hospital,Ministry of Health , Saudi Arabia
Mohammed Abdullah Al Suliman
Al-Aflaj Hospital,Ministry of Health , Saudi Arabia
Mishal Rashid Al Juma
‏Al-Aflaj General Hospital,Ministry of Health , Saudi Arabia
Nasser Saeed Alwuhayyid
Al-Aflaj General Hospital,Ministry of Health , Saudi Arabia
Zakaria Mohammed Al Muhaymid
Al-Aflaj Hospital,Ministry of Health , Saudi Arabia
Alanoud Abdullah Alhulayyil
King Khalid Hospital – Al-Kharj,Ministry of Health , Saudi Arabia
Hessa Trad Alonzay
King Khalid Hospital – Al-Kharj,Ministry of Health , Saudi Arabia

Corresponding Author(s) : Mesfer Zaid Hathal Alkhamis

mosfer1919@gmail.com

Saudi Journal of Medicine and Public Health, Vol. 2 No. 2 (2025)

Abstract

Background: Wearable biosensors integrated with artificial intelligence (AI) have significantly advanced continuous health monitoring by enabling real-time, personalized, and non-invasive assessment of physiological and behavioral parameters beyond traditional clinical environments. These technologies support disease management, early diagnosis, preventive care, and personalized interventions across diverse health domains.


Aim: This review aims to summarize recent advancements in AI-enabled wearable biosensors, focusing on their applications, methodological innovations, challenges, and future directions in biomedical engineering.


Methods: A comprehensive narrative review of recent scientific literature was conducted, analyzing developments in wearable sensor technologies, AI methodologies (including machine learning, deep learning, edge AI, federated learning, and human-in-the-loop systems), and their applications across metabolic, cardiovascular, neurological, and neonatal health domains.


Results: AI-powered wearable biosensors demonstrate high potential for continuous health monitoring, predictive analytics, and personalized intervention. Applications include glucose monitoring, cardiovascular risk detection, gait and motor assessment, and neonatal surveillance. Advances in edge computing and federated learning enhance privacy and real-time responsiveness, while digital twins and large language models improve interpretability and decision support.


Conclusion: AI-enabled wearable biosensors are transforming healthcare toward predictive, proactive, and personalized models of care, although challenges related to data privacy, robustness, biological integration, and regulation remain.

Keywords

Wearable biosensors; Artificial intelligence; Continuous health monitoring; Edge computing; Personalized medicine.
Alkhamis, M. Z. H., Fahad Mohmmed Al Thafir, Mohammed Abdullah Alskhabrah, Mohammed Razqan Matar Almutairi, Mohammed Fahad Bin Zayid, Sultan Ghusayn Aldawsari, … Hessa Trad Alonzay. (2025). Artificial Intelligence–Enabled Wearable Sensors for Continuous Health Monitoring-An Updated Review for Biomedical Engineering. Saudi Journal of Medicine and Public Health, 2(2), 2871–2881. https://doi.org/10.64483/202522489
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