Artificial Intelligence in Dental Shade Matching: A Narrative Review

Abstract

Shade selection continues to be an important factor of esthetic success in restorative and prosthetic dentistry. The limitations of traditional optical shade matching—subjective perception, light variability, and metamerism—lead to inconsistent results and unhappy patients. Although instrumental techniques like colorimeters and spectrophotometers increased reproducibility, they are still expensive and technique-sensitive. Artificial intelligence (AI) and machine learning (ML) have become revolutionary techniques in recent years for automating and standardizing dental shade matching through the analysis of optical and image-based data. Under controlled conditions, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and fuzzy logic systems have shown potential accuracy in tooth shade prediction that is comparable to instrumental approaches.The underlying principles of color science are described, current algorithms and approaches are assessed, and their clinical viability, limitations, and future prospects are discussed in this narrative review, which also synthesizes information from recent research on AI applications in dental shade matching. Prior to clinical translation, problems including dataset bias, illumination standardization, device calibration, and regulatory validation must be resolved, notwithstanding AI's great potential for objective, repeatable, and easily accessible color assessment.