Reliability of AI-Based Occlusal Adjustment Recommendations in Full Mouth Rehabilitation Cases

Abstract

Full mouth rehabilitation (FMR) is a complex prosthodontic procedure that aims to restore oral function, occlusal stability, aesthetics, and biological health in patients presenting with extensive tooth wear, multiple missing teeth, altered occlusal relationships, and compromised dentition. Occlusal adjustment is an important component of FMR because premature contacts, occlusal interferences, discrepancies in centric relation and maximum intercuspation, and inappropriate distribution of occlusal forces may compromise the longevity and function of definitive restorations. Conventional occlusal adjustment is largely dependent on clinical examination, articulating paper, shimstock, mounted diagnostic casts, intraoral evaluation, and the experience of the treating clinician.

The integration of artificial intelligence (AI) into digital dentistry has introduced new possibilities for automated occlusal analysis and treatment planning. AI-based systems can process intraoral scans, digital articulations, occlusal contact maps, three-dimensional dental models, jaw-tracking information, and other digital records to identify potentially abnormal contacts and generate recommendations for occlusal adjustment. Recent evidence indicates that AI and digital occlusal technologies may improve efficiency and reproducibility; however, their reliability varies according to the data source, scanner, software, clinical situation, and complexity of the occlusal problem.

This article discusses the principles of occlusion in full mouth rehabilitation, conventional methods of occlusal adjustment, digital occlusal analysis, applications of AI in occlusal assessment, AI-based occlusal adjustment recommendations, parameters used to assess reliability, clinician–AI agreement, advantages and limitations, and future perspectives. Particular emphasis is placed on the distinction between contact detection and clinical occlusal diagnosis, as these should not be considered equivalent. Current evidence suggests that AI-based recommendations may serve as a useful decision-support tool, but final occlusal adjustment should remain under clinician supervision.