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. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (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 Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences 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.
Highlights
The application of artificial intelligence (AI) to healthcare has increased rapidly [1]
We presented publication patterns, publish domains, research activities, author contributions, global cooperations, and cociting references of AI research on COVID-19
We identified 729 articles related to AI and COVID-19, with
Summary
The application of artificial intelligence (AI) to healthcare has increased rapidly [1]. The application of AI technology to disease detection, cancer patient screening, therapy selection, reducing medication errors, and productivity improvement is growing [3–6]. AI technology has already shown its potentiality to track the spread of coronavirus, as well as stratifying highrisk patients. It has shown great effectiveness in predicting real-time infection rates by adequately analyzing the previous data [10]. Bibliometric analysis is a quantitative analysis of academic literature to describe the trends in publications, the contributions of authors and journals, countries’ productivity, and information about research cooperations and collaborations [11–13]. Bibliometric analysis can help to monitor the trends and patterns of effective literature in various areas, including healthcare [14]
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