Abstract：
Interest in critical care-related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally.
The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords.
A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed.
The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies.
This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.
The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords.
A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed.
The number of publications increased steeply between 2018 and 2022, accounting for 72.53% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies.
This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models.
摘要：
对重症监护相关人工智能（AI）研究的兴趣正在迅速增长。然而,文献仍然缺乏全面的文献计量研究，以衡量和分析全球科学出版物。
这项研究的目的是根据出版物输出评估重症监护医学中AI的全球研究趋势。引文,国家之间的共同作者，和作者关键字的共同出现。
从Scopus数据库中检索到直到2022年3月为止的3619个文档。选择文档类型为文章后，对标题和摘要进行了资格检查。在使用VOSviewer的最终文献计量学研究中，包括1198篇论文。出版物的增长率，首选期刊，领先的研究国家，国际合作,并计算了顶级机构。
2018年至2022年间，出版物数量急剧增加，占所有收录论文的72.53％（869/1198）。美国和中国贡献了约55.17％（661/1198）的总出版物。在15个最具生产力的机构中，有9所大学跻身全球前100名。检测临床恶化，监测,预测疾病进展，死亡率,预后,对疾病表型或亚型进行分类是重症患者AI的一些研究热点。神经网络,决策支持系统，机器学习,和深度学习都是常用的AI技术。
这项研究突出了人工智能研究的热门领域，旨在改善重症监护病房的医疗保健。全面介绍了重症监护病房人工智能应用的研究趋势，并提供了对潜在合作和未来研究前景的见解。详细列出了引用次数最多的30篇文章。为了使基于AI的临床研究对于常规重症监护实践具有足够的说服力，需要进行协作研究，以提高AI驱动模型的成熟度和鲁棒性。
这项研究的目的是根据出版物输出评估重症监护医学中AI的全球研究趋势。引文,国家之间的共同作者，和作者关键字的共同出现。
从Scopus数据库中检索到直到2022年3月为止的3619个文档。选择文档类型为文章后，对标题和摘要进行了资格检查。在使用VOSviewer的最终文献计量学研究中，包括1198篇论文。出版物的增长率，首选期刊，领先的研究国家，国际合作,并计算了顶级机构。
2018年至2022年间，出版物数量急剧增加，占所有收录论文的72.53％（869/1198）。美国和中国贡献了约55.17％（661/1198）的总出版物。在15个最具生产力的机构中，有9所大学跻身全球前100名。检测临床恶化，监测,预测疾病进展，死亡率,预后,对疾病表型或亚型进行分类是重症患者AI的一些研究热点。神经网络,决策支持系统，机器学习,和深度学习都是常用的AI技术。
这项研究突出了人工智能研究的热门领域，旨在改善重症监护病房的医疗保健。全面介绍了重症监护病房人工智能应用的研究趋势，并提供了对潜在合作和未来研究前景的见解。详细列出了引用次数最多的30篇文章。为了使基于AI的临床研究对于常规重症监护实践具有足够的说服力，需要进行协作研究，以提高AI驱动模型的成熟度和鲁棒性。
