Artificial Intelligence in Orthodontics: Part 2—Data Preparation and Performance Evaluation

ABSTRACT

Artificial intelligence (AI) is increasingly integrated into orthodontic research and clinical workflows. Yet, the reliability and clinical value of these systems depend fundamentally on the quality of the data used to train them and the rigour with which their performance is evaluated. Continuing from Part 1 of this 3‐part review, this article provides a practical, implementation‐focused overview of data preparation and performance evaluation for orthodontic AI applications. Key considerations for AI models are highlighted to ensure that datasets are representative, reproducible, and clinically meaningful. Various strategies in data processing are presented to enhance generalizability while maintaining patient privacy. This review then focuses on interpreting model performance, detailing the strengths and limitations of common metrics for classification, regression, and segmentation tasks. Key concepts are discussed to guide researchers in evaluating model robustness across varied populations and imaging conditions. Finally, the article discusses the real‐world utility of trustworthy, well‐validated AI systems that can advance orthodontic diagnostics, planning, and research.