The integration of artificial intelligence (AI) into CAD/CAM workflows has revolutionized dental prosthetics manufacturing, yet its morphological trueness compared to manual design remains underexplored. This study evaluated 30 single-tooth restoration cases from 30 patients. For each case, the original clinically-approved designs were used as reference. AI designs (3Shape Automate) were compared to manual designs created by a technician (3Shape Dental System™). Morphological trueness was evaluated through 3D deviation analysis. Global surface deviations (RMSE) were compared using the Wilcoxon signed-rank test, and maximum discrepancies were compared with a paired Student's t-test, with significance set at p < 0.05. While AI demonstrated batch-processing efficiency, 6.7% of cases (2/30) with suboptimal preparation geometries required manual intervention. No significant difference was found in global surface deviation between AI (median = 79.8 μm) and manual designs (median = 68.6 μm; p = 0.1056). However, AI designs produced significantly greater maximum discrepancies (mean = 225.0 μm) compared to manual designs (mean = 184.4 μm; p = 0.0243). These findings validate AI's viability for routine restoration design but emphasize the necessity of case selection protocols and algorithm improvements for dynamic occlusion modeling to ensure comprehensive clinical adoption.
Xie et al. (2025) studied this question.