Is Large Language Model a New Hope for the Global South? A Rapid Bibliometric Synthesis of Artificial Intelligence in Anatomy Education.

Authors

Yue Lin, Yusi Huang, Junming Zhou, Xiaoqing Liu

Published in

Clinical anatomy (New York, N.Y.). Oct 30, 2026. Epub Oct 30, 2026.

Abstract

Artificial intelligence (AI), especially the recent proliferation of generative Large Language Models (LLMs), is transforming traditional anatomy education by introducing new teaching tools. However, the adoption of these cutting-edge technologies raises questions about global educational equity. It remains unclear whether AI will serve as a digital equalizer or further exacerbate marginalization. This rapid bibliometric synthesis aims to provide a comprehensive, data-driven overview of the "Application of Artificial Intelligence in Anatomy Education" (AAAE). We seek to map the intellectual landscape, detect structural and technological shifts, evaluate the geopolitical disparities, and provide corresponding references. A total of 85 consolidated records were retrieved from Web of Science and Scopus. These data constituted the whole panorama of AAAE. Bibliometrix and CiteSpace were utilized for static and dynamic mapping. A multidisciplinary expert panel validated the thematic clusters using Gwet's AC1 and the Normalized Herfindahl-Hirschman Index (HHI). Publications surged following ChatGPT's release, indicating a shift from specialized computer vision to the application of general-purpose LLMs. While high-income countries maintain leadership, AI could exert a "democratizing effect" on upper-middle-income countries, doubling their odds of publication compared to traditional anatomy. Six research frontiers and three research trajectories were captured, and corresponding implications for future researchers in the global south and north were presented. AAAE could be conducted using a stratified strategy: different expansions of AI technologies for pedagogy; leveraging commercial APIs to bypass infrastructure bottlenecks; and validating open-source models. Global equity critically demands cross-border collaboration and inclusive datasets.

PMID:
42963137
Bibliographic data and abstract were imported from PubMed on 31 Oct 2026.

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