Application of artificial intelligence in rheumatic disease: a bibliometric analysis.

IF 3.2 4区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Junkang Zhao, Linxin Li, Jie Li, Liyun Zhang
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引用次数: 0

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.

Abstract Image

人工智能在风湿病中的应用:文献计量分析。
人工智能(AI)在风湿病领域的应用提高了风湿病诊断的准确性,能够预测患者的预后,扩大了治疗方案的选择范围,并有助于提供个性化的医疗解决方案。近年来,这一领域的研究逐渐增多。因此,有必要进行文献计量分析,以阐明人工智能在风湿病领域应用的发展现状和主要研究重点。此外,确定该领域的主要贡献者及其相互关系也至关重要。本研究旨在通过文献计量学分析,调查近年来人工智能在风湿病领域应用的研究热点和合作网络。研究人员在 Web of Science 中对 2024 年 1 月 1 日前发表在 SSCI 和 SCI-EXPANDED 上的有关人工智能在风湿病中应用的文章进行了全面检索。利用 VOSviewers 和 CiteSpace 等软件工具,我们分析了包括发表年份、期刊、国家、机构和作者在内的各种参数。这种分析扩展到研究被引用的作者、生成参考文献和引文网络图,以及创建共引网络和关键词图。此外,还对该领域的研究热点和趋势进行了评估。截至 2024 年 1 月 1 日,人工智能(AI)在风湿病中的应用共发表了 3508 篇文章,年发表频率和发表率均呈稳步上升趋势。在相关论文发表方面,"Scientific Reports "成为领先期刊。就发表论文的数量而言,美国是最主要的国家,而加利福尼亚大学旧金山分校(UCSF)则是发表论文最多、被引用次数最多的机构。在作者中,Young Ho Lee 和 Valentina Pedoia 因其重大贡献而备受关注,其中 Pedoia 每篇论文的平均引用次数最高。机器学习成为突出的核心关键词。这一趋势表明,风湿病领域对人工智能研究的兴趣与日俱增,预计人工智能在该领域将发挥越来越关键的作用。本研究全面总结了人工智能(AI)在风湿病领域应用的研究趋势和发展。它深入探讨了潜在的合作和未来研究的前景,阐明了近年来的研究前沿和新兴方向。本研究的结论对研究风湿病学和免疫学的学者具有重要的参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinical and Experimental Medicine
Clinical and Experimental Medicine 医学-医学:研究与实验
CiteScore
4.80
自引率
2.20%
发文量
159
审稿时长
2.5 months
期刊介绍: Clinical and Experimental Medicine (CEM) is a multidisciplinary journal that aims to be a forum of scientific excellence and information exchange in relation to the basic and clinical features of the following fields: hematology, onco-hematology, oncology, virology, immunology, and rheumatology. The journal publishes reviews and editorials, experimental and preclinical studies, translational research, prospectively designed clinical trials, and epidemiological studies. Papers containing new clinical or experimental data that are likely to contribute to changes in clinical practice or the way in which a disease is thought about will be given priority due to their immediate importance. Case reports will be accepted on an exceptional basis only, and their submission is discouraged. The major criteria for publication are clarity, scientific soundness, and advances in knowledge. In compliance with the overwhelmingly prevailing request by the international scientific community, and with respect for eco-compatibility issues, CEM is now published exclusively online.
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