МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ ПРИМЕНЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В СТОМАТОЛОГИИ

Abstract

This article presents a theoretical and methodological analysis of artificial intelligence (AI) applications in dentistry. The distinction between algorithmic performance and clinical validity is substantiated as a fundamental prerequisite for responsible clinical integration of AI systems. The use of machine learning and deep learning methods in dental diagnostics and treatment planning is examined, along with limitations related to model generalizability, data quality, and algorithmic bias. A framework of methodological maturity levels is proposed, reflecting the transition from algorithmic testing to clinical validation and systemic integration. A three-component model (“physician – algorithm – digital infrastructure”) is developed to describe the hybrid structure of AI-assisted clinical decision-making. It is demonstrated that sustainable implementation of AI depends not only on technical performance but also on clinical validation, model interpretability, and the maturity of digital healthcare infrastructure.

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