A bibliometric analysis of artificial intelligence in anatomy education: Current situation, hot spots, and global trends

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.