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Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Author

Listed:
  • Loukas Triantafyllopoulos

    (School of Science and Technology, Hellenic Open University, 26335 Patras, Greece)

  • Evgenia Paxinou

    (School of Science and Technology, Hellenic Open University, 26335 Patras, Greece)

  • Georgios Feretzakis

    (School of Science and Technology, Hellenic Open University, 26335 Patras, Greece)

  • Dimitris Kalles

    (School of Science and Technology, Hellenic Open University, 26335 Patras, Greece)

  • Vassilios S. Verykios

    (School of Science and Technology, Hellenic Open University, 26335 Patras, Greece)

Abstract

The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI’s current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview.

Suggested Citation

  • Loukas Triantafyllopoulos & Evgenia Paxinou & Georgios Feretzakis & Dimitris Kalles & Vassilios S. Verykios, 2024. "Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis," Future Internet, MDPI, vol. 16(7), pages 1-34, June.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:7:p:221-:d:1420688
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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