Artificial Intelligence for Color Prediction and Esthetic Design in CAD/CAM Ceramic Restorations: A Systematic Review and Meta-Analyses

Background/Objectives: Artificial intelligence (AI) is increasingly embedded in CAD/CAM workflows to address persistent challenges in restorative dentistry, including unpredictable color outcomes and time-intensive crown design steps. Yet, evidence on its accuracy and efficiency remains fragmented across heterogeneous study designs and metrics. This systematic review and meta-analyses aimed to evaluate the accuracy and performance of AI for color prediction and automated crown design in CAD/CAM ceramics. Methods: A systematic review with random-effects meta-analyses. The outcomes included design time, internal fit, finish-line accuracy, color-prediction acceptability using ΔE00 (AT00), morphology deviation, and occlusal and proximal contacts. Results: Fifteen studies met the inclusion criteria. The meta-analyses showed that AI-equipped CAD reduced crown design time compared to conventional CAD (MD −88.7 s; 95% CI −134.5 to −42.9; I2 = 72%). The internal fit showed a small advantage for AI (MD −17.1 µm; 95% CI −26.2 to −7.9; I2 = 90%). For finish-line identification, the pooled mean Hausdorff distance was ~0.35 mm (95% CI 0.316–0.382; I2 = 0%). For color prediction, the pooled proportion of predictions within each study’s prespecified acceptability threshold (AT00) was near-universal (0.996; 95% CI 0.988–0.999; I2 = 0%). Morphology and functional contacts were not pooled due to incompatible metrics and units. Narrative synthesis indicated AI performance comparable to, or favorable over, conventional/technician workflows in selected regions. Conclusions: AI for CAD/CAM dentistry shows practical promise, most clearly for design-time efficiency and with encouraging signals for internal fit, finish-line identification, and color-prediction acceptability under study thresholds. However, clinical translation should proceed cautiously.