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人工智能在风湿病学应用中的文献计量分析关键指标评估

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

作者信息

Polyzou Maria, Baraliakos Xenofon

机构信息

Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece.

Rheumazentrum Ruhrgebiet, Ruhr-University Bochum, Herne, Germany.

出版信息

Rheumatol Adv Pract. 2025 Jul 7;9(3):rkaf079. doi: 10.1093/rap/rkaf079. eCollection 2025.

DOI:10.1093/rap/rkaf079
PMID:40761578
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12321292/
Abstract

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.

摘要

目的

我们的目的是评估2010年至2024年间发表的风湿病学文献中有关人工智能（AI）应用的一些有趣指标，并验证洛特卡定律和布拉德福德定律在这些应用领域中作者科研生产力方面的适用性。

方法

使用与AI在风湿病学领域应用相关的适当Scopus关键词构建数据库，并使用国际文献中相关文章里找到的公式计算各项指标。此外，运用洛特卡定律和布拉德福德定律的适用性来评估风湿病学文献计量分析的数据。

结果

计算得出的指标显示了所考虑科学领域出版物的演变和特征。所得结果表明作者合作程度较高到中等，而少数作者发表了相对大量的文章。此外，观察到的数据与理想的洛特卡分布之间存在显著偏差，且出版物的分布不符合布拉德福德分布。

结论

过去5年出版物数量的强劲上升趋势表明AI在风湿病学中具有重要意义。然而，该领域的大量工作是由少数主导科学出版物的作者开展的，这表明大多数科学家不愿涉足AI在风湿病学中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/3e112c7643d0/rkaf079f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/c22a6e7a8670/rkaf079f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/4eae600d7031/rkaf079f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/163132fd5a3e/rkaf079f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/fcd0194774b0/rkaf079f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/8bda80b14d6f/rkaf079f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/3e112c7643d0/rkaf079f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/c22a6e7a8670/rkaf079f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/4eae600d7031/rkaf079f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/163132fd5a3e/rkaf079f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/fcd0194774b0/rkaf079f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/8bda80b14d6f/rkaf079f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0ec/12321292/3e112c7643d0/rkaf079f6.jpg

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