Artificial intelligence in dentistry: clinical applications, ethical challenges and future perspectives

Introduction: Artificial intelligence (AI) has progressively transformed healthcare across multiple specialties. In dentistry, deep learning algorithms and convolutional neural networks have demonstrated diagnostic accuracy comparable to, and in certain contexts superior to, that of experienced clinicians. This narrative review synthesizes the current evidence on AI applications in dentistry, with particular attention to the ethical challenges and specific barriers faced in Latin American contexts, including Bolivia. Methods: A narrative review of the scientific literature was conducted using PubMed, Scopus, and Google Scholar. Articles published between 2025 and 2026 were included, prioritizing systematic reviews, meta-analyses, and peer-reviewed original studies. The following MeSH terms were used: "artificial intelligence," "dentistry," "ethics," "machine learning," and "oral diagnosis." Development: The first diagnostic support systems were explored in the 1980s and 1990s. The qualitative leap came with deep learning and CNNs, which allow for the analysis of radiographic images with a granularity that, in many contexts, surpasses inter-observer variability. CNN-based systems ha