The Application of Artificial Intelligence in the Field of Dental Restoration for Designing Dental Crowns: A Meta-Analysis

Background This meta-analysis was aimed at investigating the differences between dental crowns designed by artificial intelligence (AI) and those designed through traditional computer-aided design (CAD) processes to provide a reference for the clinical application of AI in the field of dental crown restoration. Methods Searches were conducted in PubMed, Web of Science, CENTRAL, EMBASE, WFPD, VIP, and CNKI to collect randomized controlled trials (RCTs) on AI-designed dental crowns and traditional CAD-based dental crowns. After the data were extracted and the risk of bias was assessed with the Cochrane Collaboration’s risk assessment tool, a meta-analysis was conducted to clarify the differences between AI-designed dental crowns and traditional CAD-based dental crowns. The search period was from the establishment of each database to January 2026. Two researchers independently screened the articles, extracted their basic information and assessed the risk of bias. The meta-analysis was conducted using Review Manager 5.3. Results A total of 12 articles were ultimately included. The meta-analysis revealed that the design time of the AI group was shorter than that of the traditional CAD group (mean difference, -279.27; 95% confidence interval, -423.18 to -135.36; P  = 0.0001), and the occlusal accuracy of the former was better than that of the traditional CAD group (mean difference, -67.39; 95% confidence interval, -132.36 to -2.42; P  = 0.04); however, no significant difference was observed between the occlusal morphology (mean difference, -0.02; 95% confidence interval, -0.06 to -0.02; P  = 0.34), and the cusp angle designed in the AI group was smaller than that of the traditional CAD group (mean difference, 3.31; 95% confidence interval, 1.12 to 5.50; P  = 0.003). Conclusions Compared with traditional CAD, AI significantly reduces the required design time while producing similar occlusal morphology results and demonstrating greater stability in occlusal accuracy. However, compared with that of the traditional CAD method, the cusp angle designed by AI still needs further optimization. Overall, as an auxiliary tool for dental crown design, AI design is suitable for clinical promotion and application. Nevertheless, it is necessary to continuously optimize the architecture of the AI algorithm and the performance of the model to enhance their applicability and service efficiency in the field of dental crown restoration, thereby providing technological support for digital dental restoration tasks.