Artificial Intelligence and Machine (Deep) Learning in Medical Education: A Bibliometric Analysis Based on VOSviewer and CiteSpace

Abstract

The complexity of the medical education field often precludes a complete reliance on explanations derived from artificial intelligence (AI). Despite the potential applications of AI, particularly those based on machine learning (ML) and deep learning (DL), in facilitating instructional support and personalized learning for medical students. Such technologies still confront substantial challenges in accurately comprehending and adapting to the multifaceted nature of medical training processes. Consequently, research into the application of AI in medical education remains an evolving field; however, few bibliometric analyses in the literature have systematically studied this area. To assist academics in grasping the future direction of teaching methodologies and educational strategies within the medical profession, this study aimed to map out the research hotspots and trends of AI and DL within medical education through bibliometric analysis. The Web of Science Core Collection provided 416 articles and reviews in the study period of 1<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> January 2000 to 3<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> April 2024. Countries, institutions, authors, references, and keywords in the field were visualized and analyzed using the VOSviewer and CiteSpace.

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