Study Maps AI Research in Anatomy Education

A rapid bibliometric synthesis of artificial intelligence in anatomy education analyzed 85 consolidated records retrieved from Web of Science and Scopus. The authors used Bibliometrix and CiteSpace for static and dynamic mapping, then asked a multidisciplinary expert panel to validate thematic clusters with Gwet's AC1 and the Normalized Herfindahl-Hirschman Index (HHI).

The synthesis found that publications surged after ChatGPT's release, marking a shift from specialized computer vision toward general-purpose large language models. High-income countries remain leaders in the research, while AI could double upper-middle-income countries' odds of publication compared with traditional anatomy.

The researchers identified six research frontiers and three research trajectories, with implications for researchers in both the global south and north. They propose a stratified approach to anatomy education research: expand AI technologies for teaching, use commercial APIs to bypass infrastructure bottlenecks, and validate open-source models. They say cross-border collaboration and inclusive datasets are critical to global equity.

A rapid bibliometric synthesis of artificial intelligence in anatomy education analyzed 85 consolidated records retrieved from Web of Science and Scopus. The authors used Bibliometrix and CiteSpace for static and dynamic mapping, then asked a multidisciplinary expert panel to validate thematic clusters with Gwet's AC1 and the Normalized Herfindahl-Hirschman Index (HHI).

The synthesis found that publications surged after ChatGPT's release, marking a shift from specialized computer vision toward general-purpose large language models. High-income countries remain leaders in the research, while AI could double upper-middle-income countries' odds of publication compared with traditional anatomy.

The researchers identified six research frontiers and three research trajectories, with implications for researchers in both the global south and north. They propose a stratified approach to anatomy education research: expand AI technologies for teaching, use commercial APIs to bypass infrastructure bottlenecks, and validate open-source models. They say cross-border collaboration and inclusive datasets are critical to global equity.