Mapping the artificial intelligence role in the design of digital prosthetics: A scoping review

Objectives: This scoping review aimed to summarize existing evidence on the application of artificial intelligence (AI) in prosthodontic design and to evaluate the automation levels of current dental CAD/CAM software. The review sought to identify which workflow steps have been automated, how automation enhances accuracy, efficiency, and consistency, and its potential benefits for implant prosthodontics. Methods: The review protocol was conducted in accordance with the PRISMA-ScR guidelines. A comprehensive search of PubMed, Scopus, Web of Science, Embase, and Cochrane Library databases, as well as the gray literature (Google Scholar and Research Gate), was performed for studies published up to September 2025. Eligible studies included original research articles addressing AI applications in dental prosthesis design. Data extraction focused on AI models used, imaging modalities, level of automation, and clinical outcomes. Additionally, commercially available dental CAD/CAM software were evaluated for their automation capabilities. Results: From 2667 records, a total of 46 studies met the inclusion criteria. Fixed prosthodontics represented the most explored area (n = 40), primarily utilizing deep learning (DL) and generative adversarial networks (GANs). AI systems demonstrated high morphological accuracy, reduced design time, and achieved clinically acceptable marginal and internal fit values. Implant and removable prosthodontics benefited from AI-driven optimization, stress analysis, and customized framework design. Among 16 CAD/CAM systems reviewed, automated margin detection and initial design proposals were common. Conclusion: AI can improve accuracy, efficiency, and reproducibility in prosthetic design. Broader clinical adoption requires validation, seamless workflow integration, diverse datasets, algorithm refinement, and multicenter trials.