Work

Predicting Pathological Dental Wear: A Roadmap for Artificial Intelligence in Natural Teeth and Restorations

Year: 2026

Type: article

Abstract: Artificial intelligence is transforming dental wear assessment, but clinically useful systems should forecast pathological progression rather than physiological wear associated with normal function and aging. Current research follows two largely separate paths: models for natural teeth detect or grade existing wear and automate comparisons of serial intraoral scans, whereas restorative-material models predict laboratory wear under controlled tribological conditions. No clinically validated system currently forecasts patient-specific future wear across natural teeth, restorations, and antagonists. This perspective proposes a multimodal longitudinal framework combining serial surface geometry with substrate identity, restoration design, occlusal function, patient behavior, and biological exposures. It defines clinically meaningful prediction targets and presents a staged roadmap for data harmonization, external validation, clinical-impact evaluation, and responsible implementation. Such systems could improve personalized recall, prevention, material selection, and timely intervention, although laboratory accuracy should not be interpreted as clinical certainty.

Source: International Dental Journal

DOI: 10.1016/j.identj.2026.111250

Author: Seyed Ali Mosaddad

Institutions: Universidad Complutense de Madrid, Shiraz University of Medical Sciences, Saveetha University

Language: English

Topic: Dental Erosion and Treatment

Subfield: General Dentistry

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,550