Artificial Intelligence is reshaping healthcare and, more specifically, orthodontics, by enabling new approaches to data analysis, diagnosis, treatment planning, and patient monitoring. Understanding both its potential and its limitations requires placing current clinical applications within the broader evolution of AI, from machine learning and deep learning to generative AI and Foundation Models. This talk provides a conceptual framework for interpreting this transformation and for understanding how different AI paradigms relate to clinical practice. It will address key technological foundations, including pattern recognition in images, videos, and biosignals, learning from annotated datasets, transfer learning, and model evaluation. Particular attention will also be paid to critical issues that directly affect the reliability and clinical adoption of AI systems, such as data quality and representativeness, bias, privacy, security, explainability, and robustness. Generative AI, Large Language Models, and Foundation Models represent a further shift in this landscape, extending AI beyond highly specialized applications toward more general-purpose systems capable of supporting a wide range of clinical, decision-support, and information-related tasks. In this context, AI can contribute across the entire care pathway, from prevention and early diagnosis to treatment planning and personalization, as well as patient adherence monitoring and follow-up. These opportunities are accompanied by major challenges involving data protection, accountability, professional deskilling, and the need for new competencies. The integration of AI into healthcare practice therefore requires robust governance frameworks aligned with the risk-based approach of the European AI Act and capable of balancing technological innovation with patient safety and ethical and regulatory compliance. AI should therefore be viewed not simply as a high-performing technology, but as an advanced support tool whose clinical value depends on how effectively, transparently, and responsibly it is integrated into healthcare processes, while preserving the central role of clinicians and patients.