A NEW bibliometric study highlights the rapid rise in artificial intelligence (AI) research within rheumatology, revealing both the field’s growing significance and the limited group of authors driving this trend.
Published research from 2010 to 2024 was analyzed to estimate key indicators of scientific productivity and collaboration in AI applications within rheumatology. Using data extracted from Scopus and assessed through established bibliometric laws, the study provides a detailed overview of current publishing patterns and author influence in the field.
The findings indicate a steep increase in AI-related rheumatology publications over the last five years, underscoring the technology’s expanding relevance in clinical and research settings. Despite this growth, the majority of publications were concentrated among a small group of prolific authors, suggesting a lack of broader engagement with AI among the wider rheumatology community.
This author dominance was assessed using Lotka’s law, which predicts that most researchers contribute only a single publication in a given field. However, the observed data deviated significantly from the expected Lotka distribution, further emphasizing that AI research in rheumatology is driven by a select few. Similarly, the study found that Bradford’s law, which describes the spread of articles across journals, did not align with current publication patterns.
The authors also examined collaboration trends and found a high to moderate degree of co-authorship, indicating that the research is often conducted in teams, although the network remains relatively concentrated.
Overall, while the results confirm an upward trajectory in AI-driven rheumatology research, they also highlight potential barriers to entry or a hesitancy among broader scientific and clinical communities to adopt AI methodologies. The study emphasizes the need for expanded participation to diversify research and strengthen future innovation in AI-powered rheumatologic care.
Reference:
Polyzou M et al. Estimation of key indicators for bibliometric analysis in the applications of artificial intelligence in rheumatology. Rheumatol Adv Pract. 2025;9(3):rkaf079.
