Xie Bi-Hua, Li Ting-Ting, Ma Feng-Ting, Li Qi-Jun, Xiao Qiu-Xia, Xiong Liu-Lin, Liu Fei
Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Department of Anesthesiology, The Third People's Hospital of Yibin, Yibin, 644000, Sichuan, China.
Perioper Med (Lond). 2024 Dec 23;13(1):121. doi: 10.1186/s13741-024-00480-x.
The application of artificial intelligence (AI) in anesthesiology has become increasingly widespread. However, no previous study has analyzed this field from the bibliometric analysis dimension. The objective of this paper was to assess the global research trends in AI in anesthesiology using bibliometric software. Literatures relevant to AI and anesthesiology were retrieved from the Web of Science until 10 April 2024 and were visualized and analyzed using Excel, CiteSpace, and VOSviewer. After screening, 491 studies were included in the final bibliometric analysis. The growth rate of publications, countries, institutions, authors, journals, literature co-citations, and keyword co-occurrences was computed. The number of publications increased annually since 2018, with the most significant contributions from the USA, China, and England. The top 3 institutions were Yuan Ze University, National Taiwan University, and Brunel University London. The top three journals were Anesthesia & Analgesia, BMC Anesthesiology, and the British Journal of Anaesthesia. The researches on the application of AI in predicting hypotension have been extensive and represented a hotspot and frontier. In terms of keyword co-occurrence cluster analysis, keywords were categorized into four clusters: ultrasound-guided regional anesthesia, postoperative pain and airway management, prediction, depth of anesthesia (DoA), and intraoperative drug infusion. This analysis provides a systematic analysis on the literature regarding the AI-related research in the field of anesthesiology, which may help researchers and anesthesiologists better understand the research trend of anesthesia-related AI.
人工智能（AI）在麻醉学中的应用日益广泛。然而，此前尚无研究从文献计量分析维度对该领域进行分析。本文的目的是使用文献计量软件评估全球麻醉学领域人工智能的研究趋势。从科学网检索截至2024年4月10日与人工智能和麻醉学相关的文献，并使用Excel、CiteSpace和VOSviewer进行可视化和分析。经过筛选，最终的文献计量分析纳入了491项研究。计算了出版物、国家、机构、作者、期刊、文献共被引频次和关键词共现的增长率。自2018年以来，出版物数量逐年增加，其中美国、中国和英国的贡献最为显著。排名前三的机构是元智大学、国立台湾大学和布鲁内尔大学伦敦分校。排名前三的期刊是《麻醉与镇痛》《BMC麻醉学》和《英国麻醉学杂志》。关于人工智能在预测低血压方面的应用研究广泛，是一个热点和前沿领域。在关键词共现聚类分析方面，关键词被分为四个聚类：超声引导区域麻醉、术后疼痛与气道管理、预测、麻醉深度（DoA）和术中药物输注。该分析对麻醉学领域与人工智能相关的研究文献进行了系统分析，有助于研究人员和麻醉医生更好地了解麻醉相关人工智能的研究趋势。
