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Review
. 2025 May 28:16:1579640.
doi: 10.3389/fendo.2025.1579640. eCollection 2025.

Global research trends in AI-assisted blood glucose management: a bibliometric study

Li Yuan #  1   2 , Yangtian Wang #  1 , Meiping Xing #  1 , Tao Liu  2 , Dan Xiang  1
Affiliations
Review

Global research trends in AI-assisted blood glucose management: a bibliometric study

Li Yuan et al. Front Endocrinol (Lausanne). .

Abstract

Background: AI-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.

Methods: A 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.

Results: The 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.

Conclusion: AI 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.

Keywords: AI; blood glucose management; continuous glucose monitoring; diabetes; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of bibliometric analysis.
Figure 2
Figure 2
Annual growth trend of publications. The annual publication trend of AI-assisted blood glucose management research from 2006 to 2025. The data for 2025 is based on publications retrieved up to January 7, 2025.
Figure 3
Figure 3
High-productivity authors’ collaboration network. Visualization of collaboration networks among authors with at least two publications. Larger nodes represent authors with higher publication counts, while edges indicate co-authorship relationships.
Figure 4
Figure 4
International Collaboration Network by Country. This network map illustrates collaborations between countries actively contributing to AI-assisted blood glucose management research, generated using VOSviewer.
Figure 5
Figure 5
Keyword co-occurrence map (CiteSpace). This map, generated using CiteSpace, visualizes the relationships between high-frequency keywords in AI-assisted blood glucose management research.
Figure 6
Figure 6
Keyword co-occurrence map (VOSviewer). This map, generated using VOSviewer, displays the co-occurrence relationships among keywords in AI-assisted blood glucose management research.
Figure 7
Figure 7
Keyword clustering map (VOSviewer). This map, generated using VOSviewer, shows clusters of keywords representing distinct research themes in AI-assisted blood glucose management.
Figure 8
Figure 8
Keyword clustering map (CiteSpace). This map, generated using CiteSpace, illustrates clusters of keywords representing major research themes in AI-assisted blood glucose management.
Figure 9
Figure 9
Burst terms in AI-assisted blood glucose management. This table visualizes keywords that experienced a sudden surge in research attention over specific periods, generated using CiteSpace.
Figure 10
Figure 10
Timeline visualization of research topics. This timeline, generated using CiteSpace, presents the evolution of major research clusters in AI-assisted blood glucose management over time.
Figure 11
Figure 11
Temporal visualization of research themes. This temporal visualization, generated using CiteSpace, showcases the progression and development of key research themes in AI-assisted blood glucose management over time.
Figure 12
Figure 12
Research hotspot heatmap.
Figure 13
Figure 13
Journal co-citation analysis.
Figure 14
Figure 14
Author co-citation analysis.
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