Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, No. 99 Longcheng Street, Taiyuan, 030032, China.
Shanxi Academy of Advanced Research and Innovation (SAARI), No.7, Xinhua Road, Xiaodian District, Taiyuan, Shanxi, China.
Clin Exp Med. 2024 Aug 23;24(1):196. doi: 10.1007/s10238-024-01453-6.
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.
人工智能（AI）在风湿性疾病中的应用提高了风湿性疾病的诊断准确性，使患者预后的预测成为可能，扩大了治疗选择，并促进了个体化医疗解决方案的提供。近年来，该领域的研究一直在不断发展。因此，需要进行文献计量分析，以阐明当前 AI 在风湿性疾病应用中的进展状况和主要研究重点。此外，确定该领域的关键贡献者及其相互关系至关重要。本研究旨在进行文献计量分析，以调查近年来 AI 在风湿性疾病应用中的当前研究热点和协作网络。在 Web of Science 中对 SSCI 和 SCI-EXPANDED 收录的关于风湿性疾病人工智能的文章进行了全面检索，检索时间截至 2024 年 1 月 1 日。利用 VOSviewer 和 CiteSpace 等软件工具，我们分析了出版年份、期刊、国家、机构和作者等各种参数。这项分析还扩展到了对被引作者、生成参考文献和引文网络图谱、创建共引网络和关键词图谱的分析。此外，还评估了该领域的研究热点和趋势。截至 2024 年 1 月 1 日，共发表了 3508 篇关于人工智能（AI）在风湿性疾病中应用的文章，其年度发表频率和比率均呈稳步上升趋势。《Scientific Reports》是相关出版物的主要期刊。就发表论文的数量而言，美国是主要国家，加利福尼亚大学旧金山分校（UCSF）是最具影响力和被引用频率最高的机构。在作者中，Young Ho Lee 和 Valentina Pedoia 因其显著贡献而受到关注，Pedia 发表的论文平均被引次数最高。机器学习是一个突出的和中心的关键词。这一趋势表明，人们对风湿性疾病的 AI 研究越来越感兴趣，预计其在该领域的作用将变得越来越重要。本研究全面总结了人工智能（AI）在风湿性疾病应用中的研究趋势和发展。它提供了对潜在合作关系和未来研究前景的洞察，阐明了近年来的研究前沿和新兴方向。本研究结果为研究风湿病学和免疫学的学者提供了有价值的参考。
