AI-based shade matching versus visual assessment: Accuracy in dental aesthetics

Visual shade selection in dentistry is inherently subjective and influenced by lighting conditions and examiner variability. Artificial intelligence (AI)-based shade matching systems provide objective, data-driven color analysis to improve consistency in aesthetic dentistry. This study compared AI-based shade matching with conventional visual assessment using a spectrophotometer as the reference standard. Sixty patients were evaluated using VITA (Vereinigte Internationale für Technologie in der Zahnmedizin) shade guides, AI software, and spectrophotometric measurements. AI achieved higher accuracy (86.7%) than visual assessment (58.3%) with statistical significance (p=0.031). Reproducibility was greater with AI (κ=0.78) compared to visual methods (κ=0.42). AI-based shade matching demonstrated superior accuracy and consistency, reducing examiner bias. Integration of AI in clinical practice may improve restorative outcomes, laboratory communication, and patient satisfaction.