Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic Review and Meta-Analysis
Ahmed A. Holiel, Mounir M. Al Nakouzi, Carlos Enrique Cuevas-Suárez, Abigailt Flores-Ledesma, Sofia Drouri, Rim BourgiObjectives
Artificial intelligence (AI)-driven automated crown design is rapidly transforming digital restorative dentistry by enabling anatomically precise and functionally integrated crowns. This systematic review and meta-analysis critically evaluate whether AI-assisted crown design systems, including machine learning (ML), deep learning (DL), generative adversarial networks (GANs), and diffusion models, produce restorations with comparable or superior morphological accuracy, occlusal integration, internal fit, and workflow efficiency relative to computer-aided design (CAD) or technician-driven workflows.
Methods
A comprehensive search of MEDLINE (PubMed), Scopus, Web of Science, Embase, and Cochrane Library was conducted for studies published through 17 March 2026. Eligible studies included in vitro, in silico, and clinical investigations comparing AI-based crown design systems with conventional workflows. Primary outcomes were morphological accuracy root-mean-square (RMS) deviation, cusp morphology, volumetric/linear deviation, occlusal contact fidelity, and internal fit; secondary outcomes included marginal adaptation and restoration design time. Risk of bias was assessed using validated tools, and meta-analyses were conducted using random-effects models with standardized mean differences (SMDs).
Results
Seventeen studies met the inclusion criteria, of which 13 were included in the quantitative synthesis. AI-based systems achieved clinically acceptable morphological accuracy, internal fit, and occlusal contact reproduction (RMS deviation: SMD = −0.15, 95% CI −1.04 to 0.74). Workflow efficiency improved significantly, with reductions in design time of 25-50% and enhanced precision in chamfer and marginal gaps (p < 0.001). DL and GAN-based platforms consistently produced crowns within clinically acceptable deviation ranges (<100–200 μm). Integration of patient-specific occlusal and mandibular dynamics further enhanced functional occlusal prediction. Expert technician refinement remained beneficial in anatomically complex cases.
Conclusions
AI-assisted crown design demonstrates promising potential for providing reproducible, morphologically accurate, and functionally integrated restorations while potentially enhancing workflow efficiency. This review underscores the potential of AI systems to standardize restorative outcomes and reduce operator dependency, while combined human-AI workflows may enhance performance in complex cases. However, the current evidence is derived predominantly from in vitro and computational studies, with limited prospective clinical validation, limited integration of patient-specific dynamic occlusal data, and insufficient long-term follow-up evidence. Therefore, the findings should be interpreted cautiously and not considered definitive evidence of clinical superiority over conventional workflows. Standardized clinical protocols and prospective trials are required to confirm long-term efficacy.