Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Abstract

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period

“…Moreover, this is the first study to fully integrate PMT, the extended TAM, and the SUS. The significant and substantial findings can be used as a theoretical foundation, particularly in designing a new mobile application for disease-control tracking worldwide [ 57 , 58 , 59 , 60 , 61 , 62 ].…”

Discovering temporal scientometric knowledge in COVID-19 scholarly production

“…There are many studies that have performed a scientometric/bibliometric evaluation of literature related to the 2019 novel coronavirus (COVID-19), with each one focusing in specific aspects, subject areas or contexts, but all of them have contributed significantly to combat this terrible pandemic and to expand the scientific knowledge. Among these works, the following studies stand out: Cai et al ( 2021 ), Casado-Aranda et al ( 2021 ), Colavizza et al ( 2021 ), Ebadi et al ( 2021 ), Fassin ( 2021 ), Grammes et al ( 2020 ), Lauper et al ( 2021 ), Malik et al ( 2021 ), Rodríguez-Rodríguez et al ( 2021 ) and Tornberg et al ( 2021 ).…”

“…Similarly to this study, Rodríguez-Rodríguez et al ( 2021 ) performed a monthly-temporal scientometric analysis of manuscripts related to SARS-CoV-2 and the use of emerging technologies (Artificial Intelligence, Machine Learning, Internet of Things, Blockchain, and others), available on WoS and Scopus databases. They analyzed how these emerging technologies are helping the task force to fight against to the pandemic.…”

Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace

“…In 25 publications, a few articles have assessed the role of technology in the specific domain of infectious diseases. Rodríguez et al Rodríguez-Rodríguez et al, 2021 explored the applications of artificial intelligence and other emerging techniques associated with intelligent data analysis for the management of the COVID-19 pandemic. Mahajan et al Mahajan and Shrivastava, 2018 conducted scientific research on the H1N1 influenza virus to extract evidence-based searching mechanisms for administrative decisions in the information science discipline.…”