Artificial intelligence for orthodontic diagnosis and treatment planning: A scoping review.

Objectives: To provide an overview of artificial intelligence (AI) applications in orthodontic diagnosis and treatment planning, and to evaluate whether AI improves accuracy, reliability, and time efficiency compared to expert-based manual approaches, while highlighting its current limitations.

Data: This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

Sources: An electronic search was performed on PubMed, Web of Science, and Embase electronic databases.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

Objectives: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images.

Search Methods: An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024.

Selection Criteria: Studies that employed AI for 2D or 3D cephalometric landmark detection were included.