Estimation of key indicators for bibliometric analysis in the applications of artificial intelligence in rheumatology.

Objectives: Our aim was to estimate some interesting indicators regarding artificial intelligence (AI) applications in rheumatology literature published between 2010 and 2024 as well as to verify the application of Lotka's law and Bradford's law for the author's scientific productivity in the field of these applications.

Methods: A database was constructed using appropriate Scopus keywords related to the application of AI in the field of rheumatology and the indices were calculated using formulas found in relevant articles in the international literature. In addition, the applicability of Lotka's law and Bradford's law was used to evaluate the data of a bibliometric analysis in rheumatology.

Results: The calculated indicators show the evolution and characteristics of publications in the scientific field under consideration. The results obtained show a high to moderate degree of author collaboration, while a small number of authors have published a relatively large number of articles. Also, a significant deviation was observed between the observed data and the ideal Lotka distribution, while the distribution of publications does not fit the Bradford distribution.

Conclusion: The strong upward trend in the number of publications over the last 5 years indicates the great importance of AI in rheumatology. However, intensive work in this field is carried out by a few authors, who dominate scientific publications, which shows the reluctance of the majority of scientists to deal with the application of AI in rheumatology.