From "Who Knows Anatomy Best?" to "What Does 'Best' Mean?": Strengthening Validation Standards for Large Language Models in Anatomy Education.

Large language models (LLMs) are increasingly benchmarked against professional examination questions and interpreted as indicators of educational competence. A recent study comparing four LLMs on publicly available anatomy multiple-choice questions reported near-ceiling performance for one model and statistically significant differences among competitors. While such comparisons are informative, interpreting "best" performance requires careful validation framing. This commentary highlights three methodological domains that critically influence inference and educational translation: (1) alignment of the inferential unit and uncertainty reporting in paired item-based designs; (2) contamination risk inherent to publicly accessible exam banks; and (3) reproducibility requirements, including transparent reporting of model configuration and evaluation conditions. Drawing on recent high-impact guidance and empirical evaluations of artificial intelligence in health and education, we propose practical refinements that would shift anatomy-LLM research from leaderboard-style comparisons toward validation science. We further argue that such refinement is a means rather than an end: for anatomy education, the decisive questions are whether model errors resemble the misconceptions of students and whether text-based accuracy transfers to three-dimensional and spatial tasks. These refinements do not diminish current findings but enhance their interpretability and educational credibility.