{"title":"从2000年到2023年麻醉人工智能研究的全球趋势:文献计量分析。","authors":"Yi Ou, Xiaoyi Hu, Cong Luo, Yajun Li","doi":"10.1186/s13741-025-00531-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Interest in artificial intelligence (AI) research in anesthesia is growing rapidly. However, there is a lack of bibliometric analysis to measure and analyze global scientific publications in this field. The aim of this study was to identify the hotspots and trends in AI research in anesthesia through bibliometric analysis.</p><p><strong>Methods: </strong>English articles and reviews published from 2000 to 2023 were retrieved from the Web of Science Core Collection (WoSCC) database. The extracted data were summarized and analyzed using Microsoft Excel, and bibliometric analysis were conducted with VOSviewer software.</p><p><strong>Results: </strong>AI research literature in anesthesia has exhibited rapid growth in recent years. The United States leads in the number of publications and citations, with Stanford University as the most prolific institution. Hyung-Chul Lee is the author with the highest number of publications. The journal Anesthesiology is highly recognized and authoritative in this field. Recent keywords include \"musculoskeletal pain\", \"precision medicine\", \"stratification\", \"images\", \"mean arterial pressure\", \" enhanced recovery after surgery\", \"frailty\", \"telehealth\", \"postoperative delirium\" and \"postoperative mortality\" indicating hot topics in AI research in anesthesia.</p><p><strong>Conclusions: </strong>Publications on AI research in the field of anesthesia have experienced rapid growth over the past two decades and are likely to continue increasing. Research areas such as depth of anesthesia (DOA) and drug infusion (including electroencephalography and deep learning), perioperative risk assessment and prediction (covering mean arterial pressure, frailty, postoperative delirium, and mortality), image classification and recognition (for applications such as ultrasound-guided nerve blocks, vascular access, and difficult airway assessment), and perioperative pain management (particularly musculoskeletal pain) have garnered significant attention. Additionally, topics such as precision medicine, enhanced recovery after surgery, and telehealth are emerging as new hotspots and future directions in this field.</p>","PeriodicalId":19764,"journal":{"name":"Perioperative Medicine","volume":"14 1","pages":"47"},"PeriodicalIF":2.0000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016147/pdf/","citationCount":"0","resultStr":"{\"title\":\"Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.\",\"authors\":\"Yi Ou, Xiaoyi Hu, Cong Luo, Yajun Li\",\"doi\":\"10.1186/s13741-025-00531-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Interest in artificial intelligence (AI) research in anesthesia is growing rapidly. However, there is a lack of bibliometric analysis to measure and analyze global scientific publications in this field. The aim of this study was to identify the hotspots and trends in AI research in anesthesia through bibliometric analysis.</p><p><strong>Methods: </strong>English articles and reviews published from 2000 to 2023 were retrieved from the Web of Science Core Collection (WoSCC) database. The extracted data were summarized and analyzed using Microsoft Excel, and bibliometric analysis were conducted with VOSviewer software.</p><p><strong>Results: </strong>AI research literature in anesthesia has exhibited rapid growth in recent years. The United States leads in the number of publications and citations, with Stanford University as the most prolific institution. Hyung-Chul Lee is the author with the highest number of publications. The journal Anesthesiology is highly recognized and authoritative in this field. Recent keywords include \\\"musculoskeletal pain\\\", \\\"precision medicine\\\", \\\"stratification\\\", \\\"images\\\", \\\"mean arterial pressure\\\", \\\" enhanced recovery after surgery\\\", \\\"frailty\\\", \\\"telehealth\\\", \\\"postoperative delirium\\\" and \\\"postoperative mortality\\\" indicating hot topics in AI research in anesthesia.</p><p><strong>Conclusions: </strong>Publications on AI research in the field of anesthesia have experienced rapid growth over the past two decades and are likely to continue increasing. Research areas such as depth of anesthesia (DOA) and drug infusion (including electroencephalography and deep learning), perioperative risk assessment and prediction (covering mean arterial pressure, frailty, postoperative delirium, and mortality), image classification and recognition (for applications such as ultrasound-guided nerve blocks, vascular access, and difficult airway assessment), and perioperative pain management (particularly musculoskeletal pain) have garnered significant attention. Additionally, topics such as precision medicine, enhanced recovery after surgery, and telehealth are emerging as new hotspots and future directions in this field.</p>\",\"PeriodicalId\":19764,\"journal\":{\"name\":\"Perioperative Medicine\",\"volume\":\"14 1\",\"pages\":\"47\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016147/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Perioperative Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13741-025-00531-x\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ANESTHESIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Perioperative Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13741-025-00531-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:人工智能(AI)在麻醉领域的研究日益受到关注。然而,缺乏文献计量学分析来衡量和分析这一领域的全球科学出版物。本研究的目的是通过文献计量分析,找出麻醉领域人工智能研究的热点和趋势。方法:从Web of Science Core Collection (WoSCC)数据库中检索2000 - 2023年发表的英文文章和综述。提取的资料采用Microsoft Excel进行汇总分析,并用VOSviewer软件进行文献计量学分析。结果:人工智能在麻醉领域的研究文献近年来增长迅速。美国在论文发表和引用数量上都处于领先地位,其中斯坦福大学(Stanford University)是最多产的机构。发表作品最多的作家是李亨哲。《麻醉学》杂志在这个领域是高度认可和权威的。最近的关键词包括“肌肉骨骼疼痛”、“精准医学”、“分层”、“图像”、“平均动脉压”、“术后增强恢复”、“虚弱”、“远程医疗”、“术后谵妄”和“术后死亡率”,这些都是麻醉人工智能研究的热点话题。结论:麻醉领域人工智能研究的出版物在过去二十年中经历了快速增长,并可能继续增加。研究领域包括麻醉深度(DOA)和药物输注(包括脑电图和深度学习)、围手术期风险评估和预测(包括平均动脉压、虚弱、术后谵妄和死亡率)、图像分类和识别(用于超声引导神经阻滞、血管通路和气道困难评估等应用)、围手术期疼痛管理(尤其是肌肉骨骼疼痛)已经引起了极大的关注。此外,精准医疗、增强术后恢复和远程医疗等主题正在成为该领域的新热点和未来方向。
Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis.
Background: Interest in artificial intelligence (AI) research in anesthesia is growing rapidly. However, there is a lack of bibliometric analysis to measure and analyze global scientific publications in this field. The aim of this study was to identify the hotspots and trends in AI research in anesthesia through bibliometric analysis.
Methods: English articles and reviews published from 2000 to 2023 were retrieved from the Web of Science Core Collection (WoSCC) database. The extracted data were summarized and analyzed using Microsoft Excel, and bibliometric analysis were conducted with VOSviewer software.
Results: AI research literature in anesthesia has exhibited rapid growth in recent years. The United States leads in the number of publications and citations, with Stanford University as the most prolific institution. Hyung-Chul Lee is the author with the highest number of publications. The journal Anesthesiology is highly recognized and authoritative in this field. Recent keywords include "musculoskeletal pain", "precision medicine", "stratification", "images", "mean arterial pressure", " enhanced recovery after surgery", "frailty", "telehealth", "postoperative delirium" and "postoperative mortality" indicating hot topics in AI research in anesthesia.
Conclusions: Publications on AI research in the field of anesthesia have experienced rapid growth over the past two decades and are likely to continue increasing. Research areas such as depth of anesthesia (DOA) and drug infusion (including electroencephalography and deep learning), perioperative risk assessment and prediction (covering mean arterial pressure, frailty, postoperative delirium, and mortality), image classification and recognition (for applications such as ultrasound-guided nerve blocks, vascular access, and difficult airway assessment), and perioperative pain management (particularly musculoskeletal pain) have garnered significant attention. Additionally, topics such as precision medicine, enhanced recovery after surgery, and telehealth are emerging as new hotspots and future directions in this field.