Related Experiment Video
Updated: Nov 9, 2025
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Artificial intelligence applications in restorative dentistry: A systematic review.
Marta Revilla-León1, Miguel Gómez-Polo2, Shantanu Vyas3
1Assistant Professor and Assistant Program Director AEGD Residency, Department of Comprehensive Dentistry, College of Dentistry, Texas A&M University, Dallas, Texas; Affiliate Faculty Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Researcher at Revilla Research Center, Madrid, Spain.
Artificial intelligence (AI) shows promise in restorative dentistry for diagnosing caries and fractures, detecting margins, and predicting failure. However, AI applications are still developing and require further clinical validation.
Area of Science:
- Restorative Dentistry
- Artificial Intelligence
- Dental Diagnostics
Background:
- The application of artificial intelligence (AI) in restorative dentistry is expanding, yet a comprehensive analysis of its current development and performance is lacking.
- Systematic documentation and evaluation of AI tools for key restorative procedures are needed.
Approach:
- A systematic review was conducted across five major databases (MEDLINE/PubMed, EMBASE, World of Science, Cochrane, Scopus) supplemented by manual searches.
- Studies were selected based on AI model application in diagnosing dental caries, vertical tooth fracture, detecting preparation margins, and predicting restoration failure.
- Study quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist, with a third investigator resolving discrepancies.
Key Points:
- 34 articles were reviewed, with the majority focusing on AI for caries diagnosis and prediction (29 studies).
- AI models demonstrated varying diagnostic accuracies: caries diagnosis (76%–88.3%), caries prediction (83.6%–97.1%), vertical tooth fracture diagnosis (88.3%–95.7%), and margin detection (90.6%–97.4%).
- AI showed high sensitivity and specificity in several diagnostic tasks, indicating potential clinical utility.
Conclusions:
- AI models offer significant potential as assistive tools in diagnosing dental caries and vertical tooth fractures.
- AI can aid in detecting tooth preparation margins and predicting restoration failure, enhancing restorative procedures.
- Despite promising results, AI applications in restorative dentistry are nascent, necessitating further research into their clinical performance.
