Artificial intelligence applied to diabetes complications: a bibliometric analysis

Area of Science:

• Medical Informatics
• Artificial Intelligence
• Bibliometrics

Background:

• Artificial intelligence (AI)-driven medical assistive technology is increasingly utilized for diagnosing, treating, and predicting diabetes complications.
• Bibliometric analysis is employed to map the research landscape of AI in diabetes complications.

Purpose of the Study:

• To explore current research trends and identify cutting-edge hotspots in the field of AI for diabetes complications.
• To provide a comprehensive overview of scientific production, key contributors, and emerging areas.

Main Methods:

• A bibliometric analysis of 935 articles published from 1988 to 2024, sourced from PubMed.
• Knowledge maps were constructed using CiteSpace, Vosviewer, and bibliometix to visualize publication trends, authors, countries, institutions, journals, and keywords.
• Data analysis included annual scientific production, author collaboration, country productivity, and prevalent research topics.

Main Results:

• Annual publications show a significant upward trend, indicating growing research interest.
• The United States, China, and India are the leading countries in AI research for diabetes complications.
• Diabetic retinopathy, diabetic nephropathy, and diabetic foot are the most frequently studied complications, with machine learning, deep learning, and screening being key research areas.

Conclusions:

• Global research on AI for diabetes complications is projected to expand.
• Future research will likely concentrate on the application of AI in diabetic retinopathy and diabetic foot management.