Artificial Intelligence in Intraoral Scanning: A Narrative Review

Intraoral scanning (IOS) enables the acquisition of three-dimensional surface models of dental and oral structures, supporting diagnosis, treatment planning and digital workflows. However, many processing steps remain operator-dependent and time-consuming. The integration of artificial intelligence (AI) with intraoral scanning (IOS) represents a rapidly evolving area in digital dentistry, aiming to automate or facilitate tasks such as tooth segmentation, landmark detection, registration and quality control. This narrative review aims to summarise technical principles, current applications, challenges, and opportunities associated with AI applied to IOS data. A narrative approach was adopted to identify and describe recent studies involving AI applications using IOS datasets. Current evidence demonstrates that automated tooth segmentation is the most developed application, with emerging uses in orthodontics, prosthodontics, surgery, and diagnostics. Despite promising performance, challenges persist, including limited dataset availability, variability across scanning systems, annotation complexity, computational demands, and reduced generalisability. Future research should prioritise standardised datasets, external validation, and clinically meaningful outcomes to support safe and effective implementation.