Integrating Large Language Models and Metaverse-Related Technologies in Urology Education: Viewpoint.
- 1Department of Urology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No. 5, Haiyuncang, Dongcheng District, Beijing, 100007, China, 86 84013150.
- 2Department of Gynecology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Abstract
Unlabelled
Urology education must prepare learners to integrate anatomical visualization, diagnostic reasoning, technical performance, communication, and ethical judgment, often under restricted working hours and limited access to high-fidelity cases. This viewpoint argues that large language models (LLMs) and metaverse-related technologies should be integrated as complementary, supervised educational tools rather than treated as autonomous replacements for educators or clinical experience. LLMs can support cognitive apprenticeship, documentation practice, assessment, and communication simulations, whereas extended reality, cloud computing, and virtual digital humans can support spatial, procedural, and remote learning. Our aim is to articulate 3 key messages: first, the educational value of these technologies depends on alignment with defined urological competencies; second, implementation should proceed in stages from governed data infrastructure to intelligent interaction and immersive multimodal training; and third, educational effectiveness must be established through prospective evaluations of transfer to clinical performance, not through technical novelty alone. We emphasize the need for rigorous randomized controlled trials, identify requirements for haptic and biomechanical fidelity, and discuss hallucination, bias, privacy, accountability, and deskilling. The responsible future of intelligent urology education will therefore require collaboration among learners, educators, clinicians, engineers, ethicists, institutions, and professional bodies.
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BibTeX
@article{li2026integrating,
title = {{Integrating Large Language Models and Metaverse-Related Technologies in Urology Education: Viewpoint.}},
author = {Peizhe Li and Huijie Gong and Heyang Liu and Siyu Han and Yiqin Lu and Yongji Yan},
journal = {JMIR medical education},
year = {2026},
month = oct,
doi = {10.2196/93235},
url = {https://europepmc.org/article/MED/42896124},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating Large Language Models and Metaverse-Related Technologies in Urology Education: Viewpoint.
AU - Peizhe Li
AU - Huijie Gong
AU - Heyang Liu
AU - Siyu Han
AU - Yiqin Lu
AU - Yongji Yan
PY - 2026
DA - 2026/10/19
JO - JMIR medical education
DO - 10.2196/93235
UR - https://europepmc.org/article/MED/42896124
AB - Unlabelled Urology education must prepare learners to integrate anatomical visualization, diagnostic reasoning, technical performance, communication, and ethical judgment, often under restricted working hours and limited access to high-fidelity cases. This viewpoint argues that large language models (LLMs) and metaverse-related technologies should be integrated as complementary, supervised educational tools rather than treated as autonomous replacements for educators or clinical experience. LLMs can support cognitive apprenticeship, documentation practice, assessment, and communication simulations, whereas extended reality, cloud computing, and virtual digital humans can support spatial, procedural, and remote learning. Our aim is to articulate 3 key messages: first, the educational value of these technologies depends on alignment with defined urological competencies; second, implementation should proceed in stages from governed data infrastructure to intelligent interaction and immersive multimodal training; and third, educational effectiveness must be established through prospective evaluations of transfer to clinical performance, not through technical novelty alone. We emphasize the need for rigorous randomized controlled trials, identify requirements for haptic and biomechanical fidelity, and discuss hallucination, bias, privacy, accountability, and deskilling. The responsible future of intelligent urology education will therefore require collaboration among learners, educators, clinicians, engineers, ethicists, institutions, and professional bodies.
SN - 2369-3762
ER -