Human anatomy remains an integral part of medical education, and recent studies have documented an emerging consensus on the key anatomical learning objectives for physicians and other health professionals in training, both at the graduate and postgraduate levels. Despite this progress, less attention has been given to assessing the clinical relevance of individual anatomical structures, and which structures students should master to achieve these learning objectives. In this study we hypothesized that published research involving individual anatomical structures is largely driven by the clinical relevance of these structures, and that tabulating the number of such publications can provide an up-to-date, evolving metric of clinical relevance. To test this hypothesis, we developed a semi-automated search routine that uses the PubMed database to quantify the publication frequency of anatomical structures and compared that to a previous study that assessed the importance of structures of the head and neck using the Delphi method, a formal procedure of generating expert consensus. Using our new approach, we were able to rank the research intensity of 2182 anatomical structures included in Grant's Dissector, a widely used textbook for

A previous paper has demonstrated a statistically significant moderate correlation between the number of citations obtained from PubMed and a Delphi study for 251 anatomical structures of the Head and Neck region, suggesting that clinical significance is a major driver of research involving anatomical structures. This raises the possibility that these ranks could be an objective measure of clinical relevance of individual anatomical structures. In the present study, we revisited the rankings of the PubMed results from the previous paper and compared it with a Delphi study for 450 musculoskeletal structures. PubMed ranks were derived using different search parameters; a PubMed search with quotations yielded a moderate, statistically significant correlation coefficient of 0.639 with the musculoskeletal dataset. Additionally, we developed a Python tool, PDF Term Search, to calculate the frequency of anatomical terms in four authoritative textbooks, and these frequencies exhibited moderate significant correlations ranging (0.549–0.646) with our PubMed‐derived ranks. We further explored strategies to improve the accuracy of our PubMed results by addressing limitations identified in the previous paper. We refined the syntax of search queries for 500 anatomical structures, resulting in marked improvement in the correlation coefficients with the musculoskeletal dataset, demonstrating clear avenues for future iterations of PubMed‐derived ranks. We also created a spreadsheet of 2181 anatomical structures ranked using PubMed, published Delphi studies, and authoritative texts, providing a resource for anatomical educators who are adjusting their curricula to better train future healthcare practitioners.