Asrın NALBANT
Medical Science and Discovery - 2026;13(7):160-166
Objective: Anatomy education is one of the fundamental building blocks of medical and health sciences curricula. A sound understanding of the structure and relationships of the human body supports clinical reasoning, examination, imaging interpretation, and procedural safety. This study aimed to analyse the most cited articles in anatomy education, identify the practices most consistently associated with effective teaching, evaluate how these practices inform an ideal anatomy curriculum and future directions, and examine the value of a constrained artificial intelligence workflow for academic content analysis. Methods: The Web of Science Core Collection was searched using predefined terms. The top 200 most cited records were screened using explicit eligibility criteria, and 66 full-text articles were included. The authors performed study selection, data extraction, thematic interpretation, and manuscript writing. ChatGPT-4o was used only to rank ideal anatomy education practices described in the supplied articles under a fixed prompt. All AI outputs were checked against the source papers. Results: The 66 articles were published between 1997 and 2023 and had a median of 118.5 citations (range, 87-472). Thirty-three articles (50.0%) were published from 2015 onward. The literature concentrated on digital and immersive technologies (37 articles, 56.1%), cadaver/dissection-related education (15 articles, 22.7%), and curriculum, clinical integration, or assessment (14 articles, 21.2%). The recurring conclusions were that clinical and imaging integration improves transfer, cadaver-based learning retains distinctive spatial and professional value, and digital resources work best as supplements within blended curricula. Conclusion: Highly cited anatomy education literature supports a hybrid curriculum rather than the replacement of one method by another. The study also shows that an LLM can assist with transparent ranking of educational practices when interpretation, verification, and final writing remain author-led.
