ARTIFICIAL INTELLIGENCE IN DENTISTRY: FUNCTIONAL CLASSIFICATION, CLINICAL RESPONSIBILITY, AND RISK STRATIFICATION

Abstract

Introduction. This study aims to develop a functional classification of artificial intelligence (AI) applications in dentistry and to propose a structured risk stratification framework based on clinical responsibility and ethical implications. The aim of study is to propose a functional classification of artificial intelligence applications in dentistry, distinguishing educational from clinical domains, and to introduce a structured risk stratification framework grounded in responsibility, autonomy, and patient safety. Methods of the Study. A conceptual analytical design was applied, integrating systematic literature analysis (2020–2025) with functional mapping of AI applications across educational and clinical domains. AI systems were categorized according to their functional role and assessed using predefined criteria, including degree of clinical responsibility, autonomy of decision-making, potential impact on patient safety, algorithmic transparency, regulatory context, and legal liability. Identified risks were stratified into low, medium, or high categories. Results. Educational AI systems, including adaptive learning platforms and simulation-based training tools, were predominantly associated with low to medium risk due to indirect clinical impact and preserved human oversight. In contrast, deep learning-based imaging systems, AI-assisted treatment planning tools, predictive analytics, and generative AI exhibited high-risk profiles, reflecting increased autonomy, limited explainability, and direct or indirect influence on clinical decision-making. A functional boundary between decision support and autonomous decision-making emerged as a critical determinant of risk. Conclusion. Risk in dental AI is primarily driven by functional responsibility rather than algorithmic sophistication. The proposed classification and risk stratification framework provide a practical foundation for ethical governance, regulatory alignment, and responsible clinical integration of AI in dentistry.