Impact of artificial intelligence-based digital smile design on patient and clinician satisfaction and facial esthetic outcomes: A systematic review and meta-analysis.

BACKGROUND

This study aimed to evaluate the influence of artificial intelligence (AI)-generated Digital Smile Design (DSD) on patients and clinician satisfaction, and overall aesthetic outcomes.

METHODS

A comprehensive search of PubMed, Scopus, Cochrane Library, ScienceDirect, and Google Scholar was conducted to identify studies (2004-2024) evaluating AI-based DSD. Eligible studies reported patient/clinician satisfaction or facial esthetic outcomes. Data were extracted on study characteristics, participant demographics, DSD type, and outcome measures. A meta-analysis of satisfaction prevalence was performed using Jamovi with a random-effects model. Methodological quality was assessed using the ROBINS-I tool, and publication bias was evaluated via rank correlation, fail-safe N, and regression tests for funnel plot asymmetry.

RESULTS

Seven studies were included after screening 387 records. AI-based tools (e.g. SmileCloud, REBEL, and Invisalign SmileView) improved esthetic outcomes, enhancing smile symmetry, lip arcs, and incisal edge visibility. Key predictors of success included philtrum height and buccal corridor. The meta-analysis showed a pooled satisfaction prevalence of 58% (95% CI: 0.30-0.86, p < 0.01), with high heterogeneity (I² = 60.23%). Studies had a moderate risk of bias, and publication bias was detected (regression test, p < 0.01).

CONCLUSION

These findings suggest that AI-generated DSD provides significant facial esthetic outcomes. However, future studies should explore the long-term sustainability of these outcomes and the cost-effectiveness of AI-based dental treatments.