Global Research Trends in Artificial Intelligence and Type 2 Diabetes Mellitus: A Bibliometric Perspective

Abstract

Artificial intelligence (AI) applied to type 2 diabetes mellitus (T2DM) is transforming the diagnosis and management of this chronic disease, posing a significant public health challenge. Despite recent advances, there remains a gap in the systematization of knowledge regarding AI and T2DM, as well as in the identification of trends and scientific collaborations in this field. This study aims to conduct a comprehensive bibliometric analysis of academic output on AI applied to T2DM, mapping the main actors, collaboration networks, and predominant research themes from 2000 to 2024. A bibliometric analysis was conducted using the Web of Science database, focusing on scientific topics related to AI applied to T2DM from 2000 to 2024. Bibliometric tools such as Bibliometrix, VOSviewer, and CitNetExplorer were utilized to examine publication patterns, co-citation networks, and keywords. The analysis included 1,454 original articles and 134 reviews, aiming to identify the most influential authors, institutions, and countries in the field. The analysis revealed a growth rate of 1.7%, with significant increases observed between 2020 and 2024. The research highlighted the use of AI for the detection of diabetic retinopathy and continuous glucose monitoring as the primary areas of publication. China (27.4%) and India (20.5%) lead scientific production and international collaborations in this field, reflecting the globalization of health research. This study provides an overview of the current state and future opportunities in AI research applied to T2DM. The findings are valuable for researchers, healthcare professionals, and academic institutions, fostering progress in AI and T2DM through collaborative and ethical strategies. This bibliometric analysis contributes to guiding the development of health research policies and optimizing the use of AI in managing T2DM.

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