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人工智能在新冠疫情中的应用:文献计量分析

Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis.

作者信息

Islam Md Mohaimenul, Poly Tahmina Nasrin, Alsinglawi Belal, Lin Li-Fong, Chien Shuo-Chen, Liu Ju-Chi, Jian Wen-Shan

机构信息

Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.

International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan.

出版信息

Healthcare (Basel). 2021 Apr 9;9(4):441. doi: 10.3390/healthcare9040441.


DOI:10.3390/healthcare9040441
PMID:33918686
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8070493/
Abstract

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. (33/729, 4.52%), (29/729, 3.97%), and (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The , , and were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

摘要

人工智能(AI)在医疗健康领域的应用有所增加,包括在新冠疫情方面的应用。本研究旨在通过纵向文献计量分析,清晰概述与新冠疫情相关的人工智能出版物趋势。我们在科学网(Web of Science)上对与人工智能在新冠疫情中的应用相关的英文同行评审文章进行了系统的文献检索。制定了检索策略以收集相关文章,并提取书目信息(如国家、研究领域、来源和作者)。使用VOSviewer(莱顿大学)和Bibliometrix(R包)来可视化作者、来源、国家、机构、全球合作、引用、共引和关键词的共现网络。我们纳入了2020年至2021年间发表的729篇关于人工智能在新冠疫情中应用的研究文章。发表这些文章最多的期刊分别是(33/729,4.52%)、(29/729,3.97%)和(29/729,3.97%)。中国(190/729,26.06%)、美国(173/729,23.73%)和印度(92/729,12.62%)是产出最多的来源国。 、 和 是产出最多的机构。这是第一项全面展示全球利用人工智能应对新冠疫情努力的研究。本研究结果还为希望未来在这些领域开展合作的学术研究人员、政策制定者和医疗从业者提供了见解和研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/7470e138bfa0/healthcare-09-00441-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/e5bdf3f71b63/healthcare-09-00441-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/72e3e8496b2a/healthcare-09-00441-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/343ff75312a2/healthcare-09-00441-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/7470e138bfa0/healthcare-09-00441-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/e5bdf3f71b63/healthcare-09-00441-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/72e3e8496b2a/healthcare-09-00441-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/343ff75312a2/healthcare-09-00441-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ecb/8070493/7470e138bfa0/healthcare-09-00441-g004.jpg

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[5]
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[6]
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[7]
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[8]
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Multimed Tools Appl. 2023-3-2

[9]
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Biomed Signal Process Control. 2023-5

[10]
Hepatic arterial infusion chemotherapy in hepatocellular carcinoma: A bibliometric and knowledge-map analysis.

Front Oncol. 2023-1-4

本文引用的文献

[1]
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment.

IEEE Access. 2020-6-12

[2]
Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.

J Healthc Eng. 2021

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Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation.

JMIR Med Inform. 2020-11-19

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Nat Commun. 2020-10-9

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Lancet Digit Health. 2020-9-18

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JMIR Mhealth Uhealth. 2020-7-27

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Artificial intelligence and COVID-19: A multidisciplinary approach.

Integr Med Res. 2020-9

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Artificial intelligence vs COVID-19: limitations, constraints and pitfalls.

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Artificial Intelligence (AI) applications for COVID-19 pandemic.

Diabetes Metab Syndr. 2020

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