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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Design quality and time efficiency of AI-based versus human-based design for tooth- and implant-supported fixed
Nareudee Limpuangthip1, Nan Hsu Myat Mon Hlaing2, Sang J Lee3
1Department of Prosthodontics, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
Objectives:
The purpose of this systematic review was to evaluate the design quality and time efficiency of fixed dental restorations generated by fully automated artificial intelligence (AI)-based systems compared with human-designed restorations.
Data Sources:
PubMed/MEDLINE, Scopus, Embase, and Cochrane Library were searched until December 2025 without language restrictions, along with a manual search of references from relevant studies.
Study Selection:
Eligible studies evaluated fully automated AI-based software for designing dental restorations, including single- or multi-unit fixed dental restorations on natural teeth or dental implants, with human-based designs as comparators. Outcomes focused on clinical qualities of the designed or fabricated restorations, comprising morphology, occlusal relationships, marginal gap, finish line, internal fit, and time efficiency. Fifteen studies met the inclusion criteria. Risk of bias in the included studies was assessed using the Methodological Index for Non-Randomized Studies (MINORS) tool.
Conclusions:
AI-designed single-unit anterior and posterior crowns were generally comparable with human designs; however, refinement of proximal contacts, occlusion, incisal guidance, and esthetic features by experienced operators was recommended due to the limitations of AI design capabilities. Inlays and three-unit prostheses showed lower clinical acceptability than human designs, while single-unit implant-supported posterior crowns demonstrated comparable outcomes within limited evidence. AI-based design consistently improved time efficiency during the design phase compared with human designs for all restoration types.
Clinical Significance:
Available evidence suggests that AI-assisted restoration design can simplify the design workflow and improve time efficiency during the design stage, particularly benefiting novice operators. However, refinement by experienced dental technicians remains necessary to achieve optimal clinically acceptable morphological and functional outcomes.
