Application of artificial intelligence in rheumatic disease: a bibliometric analysis

Area of Science:

• Rheumatology and Immunology
• Medical Informatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) applications are increasingly vital in rheumatic diseases, enhancing diagnostics, predicting patient outcomes, and personalizing treatments.
• The growing body of research necessitates a bibliometric analysis to understand current advancements and research focus areas in AI for rheumatic diseases.

Purpose of the Study:

• To conduct a bibliometric analysis of AI in rheumatic diseases.
• To identify research hotspots, trends, and collaborative networks in this field.
• To pinpoint key contributors and their interrelations.

Main Methods:

• Bibliometric analysis of 3508 articles published until January 1, 2024, sourced from Web of Science (SSCI and SCI-EXPANDED).
• Utilized VOSviewers and CiteSpace for analyzing publication year, journals, countries, institutions, authors, citations, and keywords.
• Evaluated research hotspots, trends, and collaboration networks.

Main Results:

• A consistent increase in annual publications on AI in rheumatic diseases was observed.
• "Scientific Reports" was the leading journal; the United States dominated in publications.
• University of California, San Francisco (UCSF) was the most prolific institution; Young Ho Lee and Valentina Pedoia were notable authors, with Pedoia having the highest average citations.
• Machine learning emerged as a central keyword, indicating a significant research focus.

Conclusions:

• AI research in rheumatologic diseases is rapidly expanding and becoming increasingly important.
• This study provides a comprehensive overview of research trends, frontiers, and emerging directions.
• Findings offer valuable insights for future research collaborations and scholarly endeavors in rheumatology and immunology.