Ryu JE, et al. J Adv Prosthodont. 2020 Aug;12(4):225-232. https://doi.org/10.4047/jap.2020.12.4.225

Ki-Young Lee, Kyung Su Shin, Ji-Hye Jung, Hye-Won Cho, Kyung-Hwan Kwon, Yu-Lee Kim. JKAOMS 2020;46:133-42. https://doi.org/10.5125/jkaoms.2020.46.2.133

Artificial intelligence (AI), particularly deep learning, is increasingly applied in dental prosthetics, offering new approaches to dental crown design. This scoping review aimed to summarize current evidence on AI-assisted crown design, focusing on algorithm types, dataset characteristics, and evaluation methods. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore was conducted in February 2025, covering studies published between January 2010 and February 2025. Ten studies met the inclusion criteria, of which four developed custom AI models—mainly based on generative adversarial networks—while six evaluated commercially available software. All studies used digitized dental models obtained from scanned stone casts or intraoral scans, and dataset sizes varied widely. Morphological accuracy was the most frequently reported outcome, assessed in six studies, followed by design time and occlusal contact evaluation. While most AI-generated crowns demonstrated clinically acceptable precision, only four studies fabricated physical crowns and none conducted in vivo validation. These findings suggest that AI-assisted crown design holds promise for improving anatomical accuracy and workflow efficiency, but methodological heterogeneity and the lack of clinical validation highlight the need for standardized evaluation protocols and further in vivo studies.