A bibliometric analysis of artificial intelligence in anatomy education: Current situation, hot spots, and global trends
Clinical Snapshot
PICO Framework
| P — Population | English-language publications on artificial intelligence applications in anatomy education (2005-2024) |
| I — Intervention | Bibliometric analysis using R, VOSviewer, CiteSpace, and Excel |
| C — Comparator | Not applicable (descriptive bibliometric study) |
| O — Outcomes | Research trends, publication patterns, citation impact, thematic evolution, and emerging frontiers in AI anatomy education |
Bottom Line
This bibliometric analysis maps two decades of research into AI applications in anatomy education, revealing a field that has evolved from basic educational applications to sophisticated deep learning and surgical guidance systems. While the study provides valuable insights into research trends and identifies the emergence of large language models as a new frontier, it suffers from methodological limitations including single-database searching and lack of systematic review rigor. The findings suggest growing international interest in AI-enhanced anatomy education, with the United States leading research output. For Australian medical educators, this analysis highlights emerging technologies that may enhance anatomy teaching, though individual studies would need separate evaluation before implementation. The research landscape appears to be shifting toward more sophisticated AI applications, suggesting opportunities for innovation in medical education delivery.
Key Findings
Effect Size: Not applicable for bibliometric analysis
Primary Outcome: 184 publications identified spanning 2005-2024 with thematic evolution from general medical education to specialized AI applications
Nnt Or Sensitivity: United States identified as leading contributor in publication output and citation impact
Clinical Application
High feasibility for informing educational technology adoption in Australian medical education Relevant for Australian medical schools considering AI integration in anatomy curricula, aligns with digital health initiatives and medical education innovation priorities Medical educators, anatomy teachers, curriculum developers in Australian medical schools
Abstract
INTRODUCTION: Artificial intelligence (AI) is increasingly being integrated into anatomy education, yet a comprehensive overview of the field's development and emerging trends remains lacking. This study identifies research hotspots and trends in AI applications in anatomy education through a bibliometric analysis. METHODS: English-language publications on AI in anatomy education were systematically collected from the Web of Science database. Using bibliometric tools, including R, VOSviewer, CiteSpace, and Excel, data were synthesized to explore global research trends and focal areas. RESULTS: A total of 184 papers published between 2005 and 2024 were included. The United States emerged as the leading contributor in terms of publication output and citation impact. Over the past 2 decades, research focus has shifted from general medical education applications toward AI-driven image segmentation, deep learning, and surgical guidance. More recently, large language models (e.g., ChatGPT) have begun to attract attention in anatomy education. CONCLUSION: This bibliometric analysis maps the intellectual landscape of AI in anatomy education, identifying key thematic shifts and emerging frontiers. The findings provide a foundation for guiding future research directions in this rapidly evolving field.
References
- 1.Fan, Y., Zheng, X., Yang, G., Li, P., Ran, Y., Xu, T., & Wei, T. (2026). A bibliometric analysis of artificial intelligence in anatomy education: Current situation, hot spots, and global trends. Medicine, 105(22), e49128. https://doi.org/10.1097/MD.0000000000049128
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