Three-Dimensional Printing and CAD/CAM Milling in Prosthodontics: A Scoping Review of Key Metrics Towards Future Perspectives.

: Digital prosthodontics increasingly utilize both additive (3D printing) and subtractive Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), yet comprehensive comparisons remain limited. This scoping review evaluates their relative performance across prosthodontic applications. : Systematic searches (PubMed, Scopus, Web of Science, Embase, 2015-2025) identified 28 studies (27 in vitro, 1 retrospective). Data were extracted on accuracy, efficiency, materials, and outcomes. : CAD/CAM milling demonstrated superior accuracy for fixed prostheses, with marginal gaps for milled zirconia (123.89 ± 56.89 µm), comparable to optimized 3D-printed interim crowns (123.87 ± 67.42 µm, = 0.760). For removable prostheses, milled denture bases achieved a trueness of 65 ± 6 µm, while SLA-printed dentures post-processed at 40 °C for 30 min showed the lowest root mean square error (RMSE) (30 min/40 °C group). Three-dimensional printing excelled in material efficiency (<5% waste vs. milling > 30-40%) and complex geometries, such as hollow-pontic fixed dental prostheses (FDPs) (2.0 mm wall thickness reduced gaps by 33%). Build orientation (45° for crowns, 30-45° for veneers) and post-processing protocols significantly influenced accuracy. Milled resins exhibited superior color stability (ΔE00: 1.2 ± 0.3 vs. 3D-printed: 4.5 ± 1.1, < 0.05), while 3D-printed Co-Cr frameworks (SLM) showed marginal fits of 8.4 ± 3.2 µm, surpassing milling (130.3 ± 13.8 µm). Digital workflows reduced chairside time by 29% (154.31 ± 13.19 min vs. 218.00 ± 20.75 min). All methods met clinical thresholds (<120 µm gaps). : Milling remains preferred for high-precision fixed prostheses, while 3D printing offers advantages in material efficiency, complex designs, and removable applications. Critical gaps include long-term clinical data and standardized protocols. Future research should prioritize hybrid workflows, advanced materials, and AI-driven optimization to bridge technical and clinical gaps.