From Static Atlases to Intelligent Learning Systems: A Scoping Review of Imaging-Based Tools and Emerging Artificial Intelligence Applications for Facial Aesthetic Education and Training
Abstract
Aesthetic surgery training requires anatomical knowledge, psychomotor skill, aesthetic judgment, and patient-centered decision making. Traditional training pathways face constraints as demand for aesthetic procedures increases and unregulated practice exposes patients to preventable complications. Multimodal imaging, simulation, and artificial intelligence (AI)-enabled tools may support safer, scalable training, but educational validation remains heterogeneous. This scoping review maps imaging-based tools and emerging AI applications relevant to facial aesthetic surgery education and training, including direct facial aesthetic studies and transferable technical advancements from the general reconstructive subspecialties. PubMed, MEDLINE, Embase, and the Cochrane Library were searched for English-language primary studies published from January 1, 2016, to February 28, 2026. Studies were screened by 2 reviewers and mapped by imaging scale, educational function, learner outcome, validation status, and study classification. Of 106 full-text reports assessed, 16 studies were included: 15 were in plastic surgery or facial anatomy contexts and 1 was in dermatology. Current evidence supports imaging-based visualization and simulation as promising adjuncts, but objective educational validation remains limited. Overall, this review provides a synthesis of aesthetic surgery training tools and platforms that support the training process. The continued development of high-quality imaging datasets and validated AI-assisted tools may provide an important foundation for future anatomy and aesthetic surgery education.
Level of Evidence: 5 (Diagnostic) For image description, please refer to the figure legend and surrounding text.
Related
- Applications of Artificial Intelligence-Driven Chatbots in University Level Dental Education: A Systematic Review.
- A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education.
- Artificial intelligence in biomedical visualization and education: a narrative review
- Effectiveness of an Artificial Intelligence Health Education Accurately Linking System (AI-HEALS) for the intervention of chronic kidney disease: a randomised controlled trial protocol
- Artificial Intelligence in Photodynamic Therapy: From Molecular Design of Photosensitizers to Intelligent Delivery, Adaptive Dosimetry, and Personalized Prognostication of Results
BibTeX
@article{zhang2026from,
title = {{From Static Atlases to Intelligent Learning Systems: A Scoping Review of Imaging-Based Tools and Emerging Artificial Intelligence Applications for Facial Aesthetic Education and Training}},
author = {Mengyuan Zhang and Yuyan Ma and Guojing Chang and Jiuzuo Huang and Nanze Yu and Xiao Long},
journal = {Aesthetic Surgery Journal. Open Forum},
year = {2026},
month = sep,
doi = {10.1093/asjof/ojag189},
url = {https://doi.org/10.1093/asjof/ojag189},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - From Static Atlases to Intelligent Learning Systems: A Scoping Review of Imaging-Based Tools and Emerging Artificial Intelligence Applications for Facial Aesthetic Education and Training
AU - Mengyuan Zhang
AU - Yuyan Ma
AU - Guojing Chang
AU - Jiuzuo Huang
AU - Nanze Yu
AU - Xiao Long
PY - 2026
DA - 2026/09/29
JO - Aesthetic Surgery Journal. Open Forum
DO - 10.1093/asjof/ojag189
UR - https://doi.org/10.1093/asjof/ojag189
AB - Aesthetic surgery training requires anatomical knowledge, psychomotor skill, aesthetic judgment, and patient-centered decision making. Traditional training pathways face constraints as demand for aesthetic procedures increases and unregulated practice exposes patients to preventable complications. Multimodal imaging, simulation, and artificial intelligence (AI)-enabled tools may support safer, scalable training, but educational validation remains heterogeneous. This scoping review maps imaging-based tools and emerging AI applications relevant to facial aesthetic surgery education and training, including direct facial aesthetic studies and transferable technical advancements from the general reconstructive subspecialties. PubMed, MEDLINE, Embase, and the Cochrane Library were searched for English-language primary studies published from January 1, 2016, to February 28, 2026. Studies were screened by 2 reviewers and mapped by imaging scale, educational function, learner outcome, validation status, and study classification. Of 106 full-text reports assessed, 16 studies were included: 15 were in plastic surgery or facial anatomy contexts and 1 was in dermatology. Current evidence supports imaging-based visualization and simulation as promising adjuncts, but objective educational validation remains limited. Overall, this review provides a synthesis of aesthetic surgery training tools and platforms that support the training process. The continued development of high-quality imaging datasets and validated AI-assisted tools may provide an important foundation for future anatomy and aesthetic surgery education. Level of Evidence: 5 (Diagnostic) For image description, please refer to the figure legend and surrounding text.
SN - 2631-4797
ER -