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Artificial Intelligence in Diabetic Kidney Disease Research: A Bibliometric Analysis from 2006 to 2024
ABSTRACT
Background:
Diabetic kidney disease (DKD) is a major microvascular complication of diabetes and the leading cause of end-stage renal disease worldwide. Early detection and intervention are crucial for improving patient outcomes and reducing healthcare burdens. In recent years, artificial intelligence (AI) techniques such as machine learning and deep learning have shown promise in advancing DKD research, from risk prediction to disease classification and management.To better understand the evolving landscape of AI applications in DKD, we conducted a bibliometric analysis of relevant publications from 2006 to 2024. By examining trends in publication outputs, research hotspots, and international collaborations, we aim to provide insights into the development trajectory and future directions of this rapidly growing field.
Objective:
This study aims to conduct a bibliometric analysis of research on artificial intelligence (AI) applications in diabetic kidney disease (DKD) from 2006 to 2024, examining publication trends, research hotspots, and international collaborations.
Methods:
We searched the Web of Science Core Collection for English-language articles on AI in DKD research published between 2006 and 2024. Bibliometric indicators were analyzed using CiteSpace and VOSviewer, including publication counts, country and institution contributions, keyword co-occurrences, and burst detection.
Results:
Global publications on AI in DKD research grew exponentially from 2019 onwards, with China leading in research productivity. Keyword analysis revealed an evolution of research themes from early pathological explorations to recent focuses on deep learning-based disease classification, risk prediction, and management. Chinese institutions dominated the research landscape, with increasing international collaborations observed.
Conclusions:
AI applications in DKD research have rapidly expanded and evolved, with China at the forefront. Future research should prioritize explainable AI techniques, rigorous validation strategies, and translational frameworks to bridge the gap between AI innovations and clinical practice, ultimately improving DKD management and patient outcomes.
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