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An extensive bibliometric analysis of artificial intelligence techniques from 2013 to 2023

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Abstract

Between 2013 and 2023, the scientific literature on artificial intelligence grew at an exponential rate. The subject appears to be gaining even greater popularity across a wide range of fields. Although interest in the area is now at its peak, there is a scarcity of comprehensive bibliometric study. As a result, a new perspective on the constantly changing accomplishments, contributions, and research trends is necessary. This study employed Scopus database data to investigate 11,897 AI-related papers produced between 2013 and 2023, as well as VOSviewer software to depict research trends utilizing co-citation and citation networks. The analysis found a consistent increase in AI-related papers from 2013 to 2023, with substantial increases in areas such as generative AI, explainable AI, reinforcement learning, and optimization approaches, showing their growing relevance in improving technology and tackling challenging real-world challenges. The visualized research patterns also highlight the major institutions, significant partnerships, and crucial authors in AI research, providing a more complete picture of worldwide research activities. The categorization in this study divides artificial intelligence research into five major areas: algorithmic methods, advanced machine learning techniques, data-driven automation technologies, and advanced AI methodology. Academia and industry must acknowledge these findings, which highlight the importance of ongoing research, collaboration, and advancement in artificial intelligence applications across sectors such as healthcare, finance, and manufacturing in order to foster innovation, improve decision-making, and address ethical concerns. It also provides significant insights for policymakers, educators, and business leaders, guiding the formulation of AI policies that foster responsible innovation, improve regulatory frameworks, and ensure fair access to AI developments and proposes rules that promote the ethical use of AI in content production, emphasizing transparency and equity in AI decision-making processes, thereby ensuring technical advancement and societal welfare.

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  1. Department of Computer Science & Engineering, National Institute of Technology, Raipur, CG, India

    Aditi Bajpai, Sonal Yadav & Naresh Kumar Nagwani

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  1. Aditi Bajpai
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  2. Sonal Yadav
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Bajpai, A., Yadav, S. & Nagwani, N.K. An extensive bibliometric analysis of artificial intelligence techniques from 2013 to 2023. J Supercomput 81, 540 (2025). https://doi.org/10.1007/s11227-025-07021-3

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