Artificial Intelligence in Orthodontics: Part 1-Basic Concepts

Artificial intelligence (AI) is increasingly being integrated into orthodontic research and clinical practice. This review paper consists of three parts and aims to establish a foundational understanding of AI concepts for readers from non-technical backgrounds while familiarising them with its applications and challenges within the field of orthodontics. The first part provides a comprehensive introduction to the basic concepts of AI, including definitions of machine learning and deep learning models, alongside various learning types: Supervised, unsupervised, and reinforcement learning. To explain how learning occurs and how to optimise it during model training, key features of the "learning" process are introduced. A detailed overview of supervised learning is presented, including data preparation, model selection, and performance evaluation. Following this introduction, different architectures and algorithms of machine learning and neural networks are examined, with examples of their applications in orthodontics, including benefits for clinical decision-making, image processing, and patient monitoring. Finally, this review will consider innovations in AI that hold promise for enhancing orthodontic practice, such as the use of synthetic data, transformers, generative models, and large language models.