Abstract
Selection Criteria Besides manual search, an electronic search was conducted in 5 databases: MED-LINE, Web of Science, EMBASE, Scopus, and Cochrane. The eligibility crite-ria included clinical and in vitro studies that evaluated the diagnostic perfor-mance of artificial intelligence (AI) models in restorative dentistry to detect dental caries and vertical tooth fractures, identify tooth preparation margins, and pre-dict restoration failure. On the other hand, letters to editors, studies related to robotics in dentistry, radiographic enhancement investigations, and age estima-tion model studies were excluded. Two reviewers independently screened the title and abstract, performed data ex-traction, and assessed the risk of bias in relevant articles; the discussion with the third reviewer addressed any disagreement. The Joanna Briggs Institute JBI Crit-ical Appraisal Checklist for Quasi-Experimental Evaluation was used to appraise included studies critically for their quality. Key Study Factor A review of studies in the field of restorative dentistry that developed AI models for detecting dental caries (no = 29), vertical tooth fracture (no = 2), or the tooth finishing line (no = 1), besides studies of AI models that predict restoration failure (no = 2), were evaluated. Main Outcome Measure The diagnostic accuracy based on the sensitivity and specificity of AI models as a tool for detecting dental caries, vertical tooth fracture, the tooth finishing line, and predicting restoration failure.