Artificial Intelligence Research on COVID-19 Pandemic: A Bibliometric Analysis

Abstract

The artificial intelligence literature on the COVID-19 pandemic, which negatively affects the whole world in every field, is overgrowing. This study provides a bibliometric analysis of the global scientific output of artificial intelligence research on the COVID-19 pandemic. We collected literature data from the Web of Science Core Collection. The articles, proceeding papers, reviews, and early access documents published in English up to October 9, 2021, were included in the study. We used VOSviewer, and CiteSpace to analyze the retrieved data records. Moreover, we used the h-index of the authors and the impact factor of the publications. The annual publications of artificial intelligence research on COVID-19 have significantly increased to 103.76% from 2020 (1,648) to 2021 (3,352). By October 9, 2021, 5,368 documents were determined, 5,028 of which were included in the analysis. IEEE Access (140) has published the most articles. Harvard Medical School (71) is the best institute according to the number of publications. The USA (1,297) contributed the most publications with 8,906 citations, followed by the People's Republic of China (837), India (754), England (455), and Italy (333). Zhang, Yu-Dong is the best writer with 15 publications, followed by Al-Turjman, Fadi (13), Hassanien, Aboul Ella (13), Wang, Shui-Hua (12), and Wang, Wei (11). Moreover, the top five cited documents are Wynants (2020), Li (2020b), Ozturk (2020), Apostolopoulos (2020), and Yang (2020), respectively. We hope this bibliometric analysis will be helpful for researchers, practitioners, and other interested persons to find the latest trends regarding artificial intelligence research on the COVID-19 pandemic. These findings should contribute to filling in the gaps in the field, provide new perspectives for future research, and promote collaboration.

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