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, through February 28, 2026; studies were screened by two 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. Fifteen were in plastic surgery or facial anatomy contexts and one 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.