
REVIEW article
Front. Public Health
Sec. Digital Public Health
Volume 13 – 2025 |
doi: 10.3389/fpubh.2025.1510456
Provisionally accepted
- 1 Hefei University of Technology, Hefei, China
- 2 Beijing Xiaotangshan Hospital, Beijing, China
The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devices in managing major chronic diseases such as cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes. It scrutinizes their efficacy in disease diagnosis and management when integrated with machine learning algorithms, such as ANN, SVM, RF, and deep learning models. Through an exhaustive literature review, this study dissects how these technologies aid in risk assessment, personalized treatment planning, and disease management. This research addresses a gap in the existing literature concerning the application of IoT and AI technologies in the management of specific chronic diseases. It particularly demonstrates methodological novelty by introducing advanced models based on deep learning, tight frame-based methodologies and real-time monitoring systems. This review employs a rigorous examination method, which includes systematically searching relevant databases, filtering literature that meets specific inclusion and exclusion criteria, and adopting quality assessment tools to ensure the rigor of selected studies. This study identifies potential biases and weaknesses related to data collection, algorithm selection, and user interaction. The research demonstrates that platforms integrating IoT and machine learning algorithms for chronic disease monitoring and management are not only technically viable but also yield substantial economic and social advantages in real-world applications. Future studies could investigate the use of quantum computing for processing vast medical datasets and novel techniques that merge biosensors with nanotechnology for drug delivery and disease surveillance. Furthermore, This underscores its potential and value as a future model for chronic disease management.
Keywords:
The internet of things, artificial intelligence, machine learning, deep learning, Chronic Disease
Received:
13 Oct 2024;
Accepted:
20 Jan 2025.
Copyright:
© 2025
Liu and Wang. This is an open-access article distributed under the terms of the
Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) or licensor are credited and that the
original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which
does not comply with these terms.
* Correspondence:
Boyuan Wang, Beijing Xiaotangshan Hospital, Beijing, China
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