Training the algorithmically augmented orthodontist: competencies, curriculum design, and implementation challenges.

Objectives: To establish a structured framework for incorporating artificial intelligence (AI) education into orthodontic residency training, outlining the competencies required for thoughtful and responsible use of AI in clinical practice. Materials and Methods: The framework was developed through a series of iterative discussions among orthodontic educators with experience in digital orthodontics and AI research. A targeted review of the literature in dentistry, medical education, and health care ethics was performed to identify recurring themes related to AI literacy, competency-based training, and clinical integration. Insights were synthesized into guiding principles, competency domains, and curricular components suitable for orthodontic residency programs. Results: The proposed framework describes (1) key guiding principles for integrating AI into orthodontic education, including patient-centered care, evidence-based evaluation, interdisciplinary collaboration, experiential learning, professional responsibility, and adaptability; (2) a phased curriculum spanning foundational AI concepts, clinical applications, patient management, ethics, and research appraisal; and (3) core competencies with measurable objectives focused on AI literacy, clinical workflow integration, ethical and regulatory awareness, and familiarity with AI-enabled orthodontic tools. Common implementation challenges, such as faculty experience gaps, limited resources, regulatory uncertainty, and assessment variability, are also discussed, along with practical strategies to address them. Conclusions: Integrating AI into orthodontic education is increasingly necessary to prepare future clinicians for evolving practice environments. This framework provides a practical foundation for curriculum development and encourages broader discussion to establish shared standards for AI competencies in orthodontics.