ReviewMediterranean journal of rheumatology2026
Artificial Intelligence in Rheumatology: A Comprehensive Bibliometric Analysis and Current Scientific Mapping Research.
Review in Mediterranean journal of rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The bibliometric analysis presented in this article delves into the use of Artificial Intelligence (AI) in Rheumatology, aiming to fill a gap in the existing relevant scientific literature. In the article a holistic comprehensive overview of key trends and research clusters in the field are provided, exploiting a number of widely recognised bibliometric techniques, such as citation analysis, co-authorship analysis, co-occurrence analysis, and bibliographic coupling analysis. Notably, the citation analysis reveals a diverse array of highly cited papers, underscoring the multidimensional nature of research in rheumatology. The co-authorship analysis illuminates complex collaborative networks among countries, with prominent clusters such as the European, USA and the Asian-Pacific clusters, highlighting the dynamic and interconnected nature of international collaborations. The co-occurrence analysis identifies four thematic clusters, emphasising the interconnectedness of rheumatic diseases, prediction methods, artificial intelligence algorithms considerations and patient characteristics. Addressing limitations, including the potential bias introduced by specific keywords and database restrictions, in conclusion, the article provides valuable insights for researchers, paving the way for further refinements in understanding the evolving use of AI in rheumatology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.