Artificial Intelligence in the Design and Optimization of Orthodontic Materials: A Clinical Perspective on Current State and Future Directions

Artificial intelligence (AI) has transformed orthodontic diagnosis, yet its application to orthodontic materials science remains critically underexplored. This perspective identifies and characterizes the AI–materials integration gap as the central unresolved problem in digital orthodontics: AI-optimized treatment plans are currently executed through empirically selected materials whose mechanical behavior is never modeled by the planning system. We examine four domains where this gap is consequential: thermoplastic aligner polymers (PETG vs. TPU), where supervised ANNs can predict force decay from polymer composition; NiTi archwire alloys, where Bayesian optimization and Gaussian process regression are accelerating alloy design; additive manufacturing of orthodontic devices, where supervised ML reduced print-parameter optimization burden in a 2025 five-variable surface roughness study; and AI-driven biological response prediction, where FEA-surrogate neural networks reduced biomechanical computation from minutes to milliseconds per patient query. A scoping review of clear aligner AI identified 41 studies—none addressing aligner material properties as a primary outcome. We argue that closing the AI–materials gap requires standardized open material-performance datasets; FEA-surrogate models integrating polymer stiffness as a treatment-planning input; patient-specific digital twins with defined material, mechanical, and biological parameter layers; and federated learning infrastructure spanning clinics and manufacturers.