Bibliometric Analysis of the Worldwide Scholarly Output on Artificial Intelligence in Scopus

Abstract

Introduction: the use of bibliometric analyses is useful to gain insight into the development, trends, and impact of scholarly output on artificial intelligence (AI) in several fields. Objective: to characterize the worldwide scholarly output on AI in Scopus in the period 2013-2022. Method: a descriptive observational bibliometric study was carried out. The study population consisted of the 776,961 documents identified using SciVal. The following variables were studied: number of documents (Ndoc), year of publication, annual variation rate (AVR) of the scholarly output, type of document, source, number of citations (Ncit), field-weighted citation impact (FWCI), author(s), author-level h-index, institution, country, type of collaboration, and keyphrases. Results: the scholarly output showed a steady quantitative increase during the period studied, with a positive AVR. Conference papers (68,5 %) and articles (26,5 %) were the main types of documents. Neurocomputing led the list of sources in both Ndoc (12,989) and Ncit (351,837). The highest FWCI (3.02) corresponded to Proceedings – IEEE International Conference on Robotics and Automation. China, the United States and India were the countries with the highest Ndoc by year of publication. Institutional collaboration was the most common (46,7 %) type of collaboration. The most prominent keyphrases were: Robot, Artificial Intelligence, Deep Learning, Convolutional Neural Network and Robotics. Conclusions: the scholarly production analyzed is characterized by its constant quantitative growth and is mostly represented by conference papers. Productivity and impact indicators based on citations show remarkable results. The science produced was led by China, and scientific collaboration played a relevant role.

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