Clinical performance and patient outcomes of artificial intelligence assisted shade matching in aesthetic restorations: a systematic review

Abstract

Shade selection for tooth restoration is a crucial step in achieving patient satisfaction in aesthetic dentistry. The practitioner should know the aspects of color, as well as the illumination of the object, the teeth and their surroundings and the observer himself. Given the highly subjective nature of the process, conventional shade guides were developed, along with modernized shade-taking devices, to achieve excellent color-matched restorations. Nevertheless, determining the correct shade of the restoration remains a significant challenge in routine dental practice. Artificial intelligence (AI) has emerged as a promising tool to improve the efficiency of shade matching. However, the clinical impact of its implementation and the limitations it poses remain inadequately explored. We conducted a systematic review, searching PubMed, ScienceDirect and Cochrane Library for studies. Eligible studies reported outcomes on AI performance compared with traditional triage methods. Risk of bias was assessed. The included studies (n=11) demonstrated AI’s potential to reduce triage time, improve accuracy and enhance decision support. However, limitations included under-triage risks, variable accuracy and predominance of single-center studies. In addition to implementation challenges, including workflow integration barriers and clinician acceptance. The use of AI models in esthetic dentistry improved accuracy in shade selection and restoration color prediction compared with conventional visual methods. Nevertheless, given the variability in results and the predominance of laboratory-based and single-center validation studies, large-scale clinical validation studies and standardized outcome reporting are required to synthesize evidence on long-term impact, patient-related esthetic outcomes and ethical considerations regarding the routine use of AI models.