Artificial Intelligence Designing Tooth-Supported Fixed Prosthesis: a Systematic Review
DOI:
https://doi.org/10.21270/archi.v15i2.6754Palavras-chave:
Artificial Intelligence, Computer-Aided Design, Dental Crown, Dental Prosthesis Design, Dental Internal FitResumo
Purpose: To systematically evaluate the performance of artificial intelligence (AI)–driven software in the digital design of tooth-supported fixed prostheses compared with restorations produced by dental technicians, focusing on design efficiency, occlusal morphology, internal adaptation, and functional cuspal anatomy. Materials and Methods: Electronic searches were conducted in MEDLINE/PubMed, EMBASE, Web of Science, and Scopus, with manual reference screening, to identify relevant clinical and in vitro studies published until March 2024. Studies directly comparing AI-generated designs with technician-produced digital crowns were included. Two reviewers independently extracted data and assessed methodological quality using the QUIN tool for in vitro studies. When appropriate, outcomes were pooled using random-effects models, and heterogeneity was analyzed with the I² statistic. Results: Of 1,417 records, four studies met the inclusion criteria. Quantitative analysis showed no significant differences between AI- and technician-based workflows in crown design time (MD –7.79; 95% CI –22.92 to 7.34; P = .31), occlusal morphology (MD –1.02; 95% CI –4.57 to 2.52; P = .57), or internal fit (MD –0.10; 95% CI –1.64 to 1.45; P = .90). AI-generated crowns demonstrated a more consistent functional cuspal angle (MD 0.52; 95% CI 0.19 to 0.85; P = .002). High heterogeneity (I² > 96%) was observed. Conclusion: AI-assisted crown design performs comparably to experienced technicians and may improve specific morphological features. However, the small number of studies and substantial heterogeneity highlight the need for standardized clinical research.
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