PERSPECTIVE article
Front. Endocrinol.
Sec. Clinical Diabetes
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1571362
This article is part of the Research TopicWorld Diabetes Day 2024: Exploring Mechanisms, Innovations, and Holistic Approaches in Diabetes CareView all 7 articles
Continuous Glucose Monitoring Combined with Artificial Intelligence: Redefining the Pathway for Prediabetes Management
Provisionally accepted
- 1Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- 2Heilongjiang Academy of Chinese Medical Sciences, Harbin, China
- 3China Science and Technology Development Center for Chinese Medicine, Beijing, China
- 4Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Science, Dongcheng, China
- 5Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
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Prediabetes represents an early stage of glucose metabolism disorder with significant public health implications. Although traditional lifestyle interventions have demonstrated some efficacy in preventing the progression to type 2 diabetes, their limitations-such as lack of personalization, restricted real-time monitoring, and delayed intervention-are increasingly apparent. This article systematically explores the potential applications of continuous glucose monitoring (CGM) technology combined with artificial intelligence (AI) in the management of prediabetes. CGM provides real-time and dynamic glucose monitoring, addressing the shortcomings of conventional methods, while AI enhances the clinical utility of CGM data through deep learning and advanced data analysis. This review examines the advantages of integrating CGM and AI from three perspectives: precise diagnosis, personalized intervention, and decision support. Additionally, it highlights the unique roles of this integration in remote monitoring, shared decision-making, and patient empowerment.The article further discusses challenges related to data management, algorithm optimization, ethical considerations, and future directions for this technological integration. It proposes fostering multidisciplinary collaboration to promote the application of these innovations in diabetes management, aiming to deliver a more precise and efficient health management model for individuals with prediabetes.
Keywords: Continuous glucose monitoring, prediabetes, artificial intelligence, type 2 diabetes mellitus, Fasting blood glucose
Received: 05 Feb 2025; Accepted: 06 May 2025.
Copyright: © 2025 Ji, Liu, Zhang and You. 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: Jiale Zhang, China Science and Technology Development Center for Chinese Medicine, Beijing, China
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