Application of artificial intelligence in rheumatic disease: a bibliometric analysis

Abstract

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. "Scientific Reports" emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Keywords: Artificial intelligence; Bibliometrics; CiteSpace; Machine learning; Rheumatic diseases; VOS viewers.

Fig. 1

Trends in the growth of publications and the number of citations

Fig. 2

Co-authorship network of countries

Fig. 3

Co-authorship network of institutions

Fig. 4

Co-authorship network of authors

Fig. 5

The co-occurrence cluster analysis of Keywords

Fig. 6

Top 15 keywords with strong citation bursts between 2014 and 2024