Development of generative artificial intelligence in medical education: a bibliometric profiling

Abstract

While studies on generative artificial intelligence (GAI) in medical education have attracted increased attention, there exists a gap in the literature in this field. To gain insight into the research trends and focal areas in this field, a bibliometric analysis of studies on GAI-related medical education was conducted using the Web of Science database over the past 2 years. Based on a search strategy, 281 relevant articles were selected for analysis using the analytical tool CiteSpace. The aim of these analyses was to identify the main trends, categories, countries, institutions, journals, and keywords in this field, while also assessing the impact of these GAI-related medical education studies. This approach ultimately revealed that the GAI technologies are integrated with medical education, as evidenced by the CiteSpace analysis. The analysis of noun phrases and keyword co-occurrence provides insights into specific clusters of interest, important themes, and relationships in the GAI-related medical education field. Together, the results of this bibliometric analysis provide high-level insights into the progression, development, and broader implications of research focused on GAI-related medical education, providing a foundation that can help guide studies and the direction of GAI-related medical education research in the future.