Scoping review demonstrates strong computational performance but limited clinical translation for dental artificial intelligence, highlighting the need for prospective multicenter validation.
Background/Objectives: Artificial intelligence (AI) is transforming prosthodontic procedures into data-driven support systems. This scoping review systematically maps the literature using an evidence maturity framework, distinguishing it from recent applications-focused compilations by Aljulayfi, Schwendicke, and others. Methods: A comprehensive search of PubMed, Scopus, and Web of Science (January 2023–March 2026) was conducted in accordance with PRISMA-ScR guidelines, supplemented by targeted manual citation tracking. Fifty studies were included in the review. Results: The evidence base comprises approximately half primary research (computational, in vitro, clinical) and half review articles. Foundational diagnostic CNNs report high technical performance metrics (i.e., sensitivity 98.67%, precision 78.12%), while generative CAD/CAM approaches reduce design time by up to 52% in vitro. However, the evidence synthesis reveals a significant gap between algorithmic efficacy and clinical utility. Most applications currently reside at the “technical validation” stage, while emerging domains such as large language models remain largely experimental. Conclusions: Impressive computational metrics do not automatically equate to patient-centered clinical benefits. Current AI systems must be regarded as adjunctive decision-support instruments rather than autonomous tools. Future research must prioritize prospective multicenter validation, implementation science, explainability, and robust regulatory oversight.
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Boca et al. (2026) studied this question.
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