Thematic Evolution in Medical Education Research (2000-2024): A Large-Scale BERTopic Analysis

Few large-scale, semantically informed mappings of medical education research exist: traditional methods such as keyword co-occurrence and Latent Dirichlet Allocation cannot capture the contextual meaning of scientific abstracts. This study aimed to identify major research topics in medical education, classify their temporal trends, explore temporal associations with major policy and technology developments, and characterize their structural positioning.

We applied BERTopic, a transformer-based topic modeling framework, to 276,253 English-language publications from PubMed, Web of Science, Scopus, and OpenAlex (2000-2024). Documents were embedded with a sentence-transformer language model and clustered into topics; labels were independently reviewed by two authors. Trends in each topic's relative share were classified using Poisson regression with an offset for total annual output, autocorrelation-robust Newey-West standard errors, and false discovery rate correction. Topic positioning used an embedding-based centrality-density heuristic.

BERTopic identified 39 topics (7.0% outliers); Topic 0 was a residual, non-substantive cluster excluded from interpretation. Among the 38 substantive topics, 10 showed significantly increasing relative shares (Hot; including AI/ChatGPT, virtual reality simulation, burnout and wellbeing, diversity and equity, empathy, and climate and health education), 12 showed decreasing shares (Cold), and 16 were Stable. Classifications agreed across alternative standard-error specifications for 35 of 38 topics; simulation-based education, clinical imaging, and interprofessional education were significant only under less conservative errors. Temporal associations with policy and technology reference periods were exploratory and not causal. The positional map distinguished established themes from emerging, terminologically unsettled niches.

The relative emphasis of medical education research has gradually shifted toward technology-enhanced and socially responsive themes, while foundational topics continue to grow in absolute volume. These findings offer one data-driven, assumption-dependent lens on the field's evolution to inform curriculum planning, research prioritization, and policy.