SynthesisBMC oral health2026
The application of artificial intelligence in the field of dental restoration for designing dental crowns: a meta-analysis.
Synthesis in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
5 authors.
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Abstract
backgroundThis meta-analysis aimed to investigate 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.
methodsSearches were conducted in PubMed, Web of Science, CENTRAL, EMBASE, WFPD, VIP, and CNKI to collect in vitro studies 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 Quality Assessment Tool for In Vitro Studies (QUIN Tool), a meta-analysis was conducted to clarify the differences between AI-designed dental crowns and traditional CAD-based dental crowns. The search period ranged 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. Between-study heterogeneity was assessed using Cochrane Q and I² statistics.
resultsA 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, -289.03; 95% confidence interval, -450.18 to -127.88; P = 0.0004), 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 discrepancy (mean difference, -0.02; 95% confidence interval, -0.06 to 0.02; P = 0.34), and the cusp angle in the AI group was smaller than that in the traditional CAD group (mean difference, 3.31; 95% confidence interval, 1.12 to 5.50; P = 0.003).
conclusionsCompared 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, AI design shows promise as an auxiliary tool for dental crown design, but further algorithm optimization and high-quality clinical trials are needed to confirm its long-term clinical performance.
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