Artificial Intelligence–Enhanced Virtual Articulators: A Narrative Literature Review

Abstract

Accurate analysis of mandibular dynamics is fundamental to prosthodontic rehabilitation. Mechanical articulators replicate only simplified jaw movements and do not account for neuromuscular influences. Virtual articulators improve digital simulation but remain restricted by static algorithms and operator-dependent inputs. Artificial intelligence (AI) provides an opportunity to enhance digital occlusal modelling through predictive simulations and adaptive learning. This narrative review synthesizes evidence (2010–2024) regarding AI integration in virtual articulators and analyzes technological advancements, clinical benefits, current limitations, and future research needs.

Electronic searches were carried out using PubMed, Scopus, Web of Science, and Google Scholar. Search terms included “virtual articulator,” “artificial intelligence,” “mandibular kinematics,” and “digital prosthodontics.” Studies focusing on AI-based mandibular modelling or occlusal simulation were eligible.

AI-enhanced virtual articulators consistently demonstrated superior accuracy in condylar trajectory reproduction, occlusal-contact prediction, parafunctional pattern recognition, and biomechanical load analysis. They reduce operator variability, improve restorative predictability, and enhance digital workflows. Limitations include dataset heterogeneity, absence of standardized protocols, and reduced interpretability of deep-learning models. In conclusion, AI-supported virtual articulators hold considerable potential for increasing the precision and personalization of prosthodontic treatment, although further validation through clinical trials is required.