OBJECTIVES: The aim of this study was to examine the current clinical adoption and curriculum-wise educational integration of teleorthodontics in postgraduate orthodontic residency programs in North America. This study further examined the adoption of AI-driven features of teleorthodontics and their perception at the institutional level. METHODS: A survey consisting of 20 questions was constructed and distributed using Qualtrics XM to all 74 accredited postgraduate orthodontic residency programs in North America. The data were analyzed using the IBM Statistical Package for the Social Sciences (SPSS). RESULTS: Sixty-eight surveys were successfully delivered, and 41 were returned completed, yielding a response rate of 60.3%. Of the 41 respondents, 13 (31.7%) programs reported providing teleorthodontics training, and 6 (46.2%) of those programs reported including training on AI-driven features in teleorthodontics, such as 3D clinical crown movement analysis, aligner and fixed-appliance progress tracking, oral health assessment, and compliance coaching. Twelve (29.3%) reported currently implementing teleorthodontics in clinical practice, and among those programs, the majority (7/12, 58.3%) reported using AI-driven features. CONCLUSIONS: Postgraduate orthodontic residency programs that have adopted teleorthodontics perceive it positively. They generally agree that teleorthodontics reduces the number of in-person visits and that its AI-driven features are reliable. However, only about one-third of the responding programs currently implement teleorthodontics in clinical practice and educational curricula. Among those programs that have not yet implemented teleorthodontics for clinical use and resident training, the main barriers were lack of institutional priority, data security or privacy concerns, and limited available curriculum time. In contrast to the individual-level adoption rate, the integration of teleorthodontics at the institutional-level appears to be progressing more gradually, with similar trends observed in its AI-driven features.
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Kim et al. (2026) studied this question.
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