# A Systematic Review of the Accuracy of Crowns Designed Using Artificial Intelligence Versus CAD/CAM and Traditional Methods

## TL;DR

This paper reviews how accurate crowns made with AI compare to traditional and CAD/CAM methods in dentistry.

## Contribution

The study systematically compares AI-assisted crown design accuracy against conventional and CAD/CAM workflows.

## Key findings

- AI-assisted crowns showed clinically acceptable fit and margin adaptation, comparable to CAD/CAM systems.
- Occlusal accuracy of AI models was similar to CAD/CAM and technician designs, though with some variability.
- AI is a reliable tool to enhance precision in crown design but not a replacement for clinical expertise.

## Abstract

Background and Objectives: Advances in digital dentistry, particularly CAD-CAM, have improved the efficiency and precision of crown design and fabrication. Recently, artificial intelligence (AI)-integrated CAD-CAM systems have enabled automated tooth morphology generation, margin detection, and occlusal analysis, enhancing consistency and accuracy. This systematic review evaluates the accuracy of AI-assisted crown design compared with conventional and CAD-CAM workflows. Materials and Methods: A systematic search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Cochrane, and LILACS for studies published between January 2010 and December 2025 that assessed the marginal fit, internal adaptation, and occlusal contact accuracy of single crowns. Screening, full-text assessment, and data extraction followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Methodological quality and risk of bias were evaluated using the Modified CONSORT checklist for in vitro studies and the Joanna Briggs Institute tools for clinical studies. Results: Of 887 records identified, 12 studies met the inclusion criteria. Nine studies showed a moderate risk of bias, two moderate-to-high, and one low-to-moderate. AI-assisted crown design demonstrated clinically acceptable internal fit and marginal adaptation, comparable or superior to CAD-CAM systems. Occlusal contact accuracy was generally comparable to CAD-CAM and technician-designed crowns, though variability was observed across AI models. Conclusions: AI-assisted crown design provides a reliable fit and marginal adaptation, with occlusal accuracy approaching conventional CAD-CAM and technician workflows. While not a replacement for clinical expertise, AI serves as a valuable adjunct, enhancing reproducibility, precision, and overall quality in restorative dentistry. Further standardized clinical studies are needed to validate long-term outcomes and optimize occlusal performance.