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Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis
BACKGROUND: 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. OBJECTIVE: The objective of this study was to assess the g...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752463/ https://www.ncbi.nlm.nih.gov/pubmed/36449345 http://dx.doi.org/10.2196/42185 |
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author | Tang, Ri Zhang, Shuyi Ding, Chenling Zhu, Mingli Gao, Yuan |
author_facet | Tang, Ri Zhang, Shuyi Ding, Chenling Zhu, Mingli Gao, Yuan |
author_sort | Tang, Ri |
collection | PubMed |
description | BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-9752463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-97524632022-12-16 Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis Tang, Ri Zhang, Shuyi Ding, Chenling Zhu, Mingli Gao, Yuan J Med Internet Res Original Paper BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. JMIR Publications 2022-11-30 /pmc/articles/PMC9752463/ /pubmed/36449345 http://dx.doi.org/10.2196/42185 Text en ©Ri Tang, Shuyi Zhang, Chenling Ding, Mingli Zhu, Yuan Gao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.11.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Tang, Ri Zhang, Shuyi Ding, Chenling Zhu, Mingli Gao, Yuan Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis |
title | Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis |
title_full | Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis |
title_fullStr | Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis |
title_full_unstemmed | Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis |
title_short | Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis |
title_sort | artificial intelligence in intensive care medicine: bibliometric analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752463/ https://www.ncbi.nlm.nih.gov/pubmed/36449345 http://dx.doi.org/10.2196/42185 |
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