Global research trends in AI-assisted blood glucose management: a bibliometric study - Yuan, Li; Wang, Yangtian; Xing, Meiping; Liu, Tao; Xiang, Dan (Frontiers in Endocrinology, 2025)
BackgroundAI-assisted blood glucose management has become a promising method to enhance diabetes care, leveraging technologies like continuous glucose monitoring (CGM) and predictive models. A comprehensive bibliometric analysis is needed to understand the evolving trends in this research area.MethodsA bibliometric analysis was performed on 482 articles from the Web of Science Core Collection, focusing on AI in blood glucose management. Data were analyzed using CiteSpace and VOSviewer to explore research trends, influential authors, and global collaborations.ResultsThe study revealed a substantial increase in publications, particularly after 2016. Major research clusters included CGM, machine learning algorithms, and predictive modeling. The United States, Italy, and the UK were prominent contributors, with key journals such as Diabetes Technology & Therapeutics leading the field.ConclusionAI technologies are significantly advancing blood glucose management, especially through machine learning and predictive models. Future research should focus on clinical integration and improving accessibility to enhance patient outcomes.