Role of Generative AI in Personalized Smile Design: A Clinical Evaluation of Esthetic Outcomes

Abstract

Background Digital Smile Design (DSD) has become an important component of contemporary esthetic dentistry because it permits visualization of proposed dental changes before irreversible treatment. Generative artificial intelligence (GenAI) may further enhance this process by rapidly producing individualized smile simulations. However, the esthetic value of AI-generated designs compared with conventional clinician-designed and clinician-refined designs remains incompletely established. Aim To evaluate the esthetic acceptability of generative-AI-assisted personalized smile designs and compare conventional Digital Smile Design, AI-generated smile design, and clinician-refined AI-assisted smile design. Materials and Methods A prospective comparative study was designed involving 60 adult participants seeking esthetic dental treatment. Standardized facial and intraoral photographs were obtained. Three smile-design conditions were produced: conventional clinician-generated DSD, AI-generated DSD, and AI-generated DSD subsequently refined by an experienced clinician. Patients and dental professionals independently evaluated the designs using a 10-point visual analogue scale (VAS), satisfaction assessment, and paired-preference testing. Objective digital parameters and design-generation time were also recorded. Repeated-measures data were analyzed using the Friedman test with pairwise comparisons. Results Illustrative simulated results: Mean patient VAS scores were 7.39 ± 0.64 for conventional DSD, 7.74 ± 0.76 for AI-generated DSD, and 8.37 ± 0.71 for AI-refined DSD. The overall difference was statistically significant (Friedman test, P < 0.001). Professional ratings were 7.89 ± 0.68, 7.35 ± 0.77, and 8.64 ± 0.76, respectively (P < 0.001). Mean design times were 62.4 ± 11.6, 24.8 ± 6.4, and 35.6 ± 7.8 minutes, respectively. Conclusion The illustrative findings suggest that generative AI may improve smile-design efficiency, while clinician refinement may further improve esthetic acceptance. The findings support an AI-assisted rather than AI-replacement model of personalized smile design. Actual patient data must replace the simulated dataset before scientific submission.