M. Cascella, Francesco Perri, A. Ottaiano, A. Cuomo, S. Wirz, S. Coluccia
{"title":"Trends in Research on Artificial Intelligence in Anesthesia: A VOSviewer -Based Bibliometric Analysis","authors":"M. Cascella, Francesco Perri, A. Ottaiano, A. Cuomo, S. Wirz, S. Coluccia","doi":"10.4114/intartif.vol25iss70pp126-137","DOIUrl":null,"url":null,"abstract":"Background: The scientific literature on Artificial Intelligence (AI) in anesthesia is rapidly growing. Considering that applications of AI strategies can offer paramount support in clinical decision processes, it is crucial to delineate the research features. Bibliometric analyses can provide an overview of research tendencies useful for supplementary investigations in a research field. Methods: The comprehensive literature about AI in anesthesia was checked in the Web of Science (WOS) core collection. Year of publication, journal metrics including impact factor and quartile, title, document type, topic, and article metric (citations) were extracted. The software tool VOSviewer (version 1.6.17) was implemented for the co-occurrence of keywords and the co-citation analyses, and for evaluating research networks (countries and institutions). Results: Altogether, 288 documents were retrieved from the WOS and 154 articles were included in the analysis. The number of articles increased from 4 articles in 2017 to 37 in 2021. Only 34 were observational investigations and 7 RCTs. The most relevant topic is “anesthesia management”. The research network for countries and institutions shows severe gaps. Conclusion: Research on AI in anesthesia is rapidly developing. Further clinical studies are needed. Although different topics are addressed, scientific collaborations must be implemented.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inteligencia Artif.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4114/intartif.vol25iss70pp126-137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Background: The scientific literature on Artificial Intelligence (AI) in anesthesia is rapidly growing. Considering that applications of AI strategies can offer paramount support in clinical decision processes, it is crucial to delineate the research features. Bibliometric analyses can provide an overview of research tendencies useful for supplementary investigations in a research field. Methods: The comprehensive literature about AI in anesthesia was checked in the Web of Science (WOS) core collection. Year of publication, journal metrics including impact factor and quartile, title, document type, topic, and article metric (citations) were extracted. The software tool VOSviewer (version 1.6.17) was implemented for the co-occurrence of keywords and the co-citation analyses, and for evaluating research networks (countries and institutions). Results: Altogether, 288 documents were retrieved from the WOS and 154 articles were included in the analysis. The number of articles increased from 4 articles in 2017 to 37 in 2021. Only 34 were observational investigations and 7 RCTs. The most relevant topic is “anesthesia management”. The research network for countries and institutions shows severe gaps. Conclusion: Research on AI in anesthesia is rapidly developing. Further clinical studies are needed. Although different topics are addressed, scientific collaborations must be implemented.
背景:人工智能(AI)在麻醉中的科学文献正在迅速增长。考虑到人工智能策略的应用可以在临床决策过程中提供最重要的支持,描绘研究特征至关重要。文献计量学分析可以提供对研究领域的补充调查有用的研究趋势的概述。方法:在Web of Science (WOS)核心馆藏中查阅人工智能在麻醉中的综合文献。提取出版年份、期刊指标(包括影响因子和四分位数)、标题、文献类型、主题和文章指标(引用)。利用软件VOSviewer(版本1.6.17)进行关键词共现和共被引分析,并对研究网络(国家和机构)进行评价。结果:从WOS中共检索到288篇文献,154篇纳入分析。文章数量从2017年的4篇增加到2021年的37篇。只有34项观察性调查和7项随机对照试验。最相关的话题是“麻醉管理”。国家和机构的研究网络显示出严重的差距。结论:人工智能在麻醉领域的研究正在迅速发展。需要进一步的临床研究。虽然讨论了不同的主题,但必须实施科学合作。