Mapping artificial intelligence in problem-based and case-based medical education: a bibliometric analysis (2019–2026)

Abstract

Introduction Problem-based learning (PBL) has been a cornerstone of medical education since its introduction at McMaster University in the 1960s. Since the public release of ChatGPT in November 2022, artificial intelligence (AI) tools have increasingly been applied to PBL and case-based learning (CBL) contexts, yet the research landscape at this intersection remains poorly characterized. This study aimed to map the growth trajectory, thematic structure, and collaboration networks of AI-PBL/CBL research from 2019 to 2026. Methods A comprehensive search of Scopus and Web of Science was conducted on June 2, 2026, combining AI-related terms with PBL/CBL frameworks and medical education contexts. Using a PRISMA-guided bibliometric review workflow, 1,616 records were identified; after deduplication and eligibility screening, 735 unique publications (2019–2026, original articles, reviews, conference papers, and other eligible indexed document types) were included. Bibliometric analyses employed VOSviewer for network visualization (keyword co-occurrence, co-authorship, co-citation), CiteSpace for citation burst detection, and Bibliometrix for thematic mapping, three-field plot, and factorial analysis. Results Publication output grew from 25 papers in 2022 to 254 in 2025, with 206 papers indexed by June 2, 2026. The United States (n = 70) and China (n = 64) led publication volume. Keyword co-occurrence analysis identified four thematic clusters: a central AI-focused cluster, a medical education and clinical reasoning cluster, a nursing and simulation-oriented cluster, and an educational technology cluster. Kung et al.'s 2023 study evaluating ChatGPT's performance on the USMLE was the most frequently co-cited reference (62 co-citations, betweenness centrality = 0.11). Thematic mapping positioned machine learning as a motor theme, while clinical reasoning, medical education, and self-directed learning appeared in the basic themes quadrant. The country/region collaboration network comprised 33 countries/regions and was led by the United States and China, although collaboration patterns remained uneven across regions. Conclusion To our knowledge, this is the first bibliometric study specifically focused on the intersection of AI technologies with PBL/CBL in health professions education. The findings reveal rapid growth after 2023, four distinct but interconnected research clusters, and a collaboration network led by the United States and China, with uneven regional participation. These results may inform curriculum design and research priorities in AI-enhanced medical education.