A deep learning system on monolithic implant‐supported crown design: Evaluating AI‐generated models against conventional software outputs

Purpose To investigate the effectiveness of a novel transformer‐based deep learning model for generating monolithic implant‐supported crowns (ISCs), comparing its performance to traditional software‐generated crowns. Materials and Methods A total of 311 patients who received single implant placements in the posterior first molar area were included. Digital impressions as well as technician‐designed crowns were imported into the AI model. The PoinTr architecture‐based AI model was trained on 291 cases and validated using 20 additional cases. Four groups of ISC designs were evaluated: Group 1, Software1‐generated crowns; Group 2, Software 2‐generated crowns; Group 3, AI‐generated crowns; and Group 4, technician‐designed crowns. Accuracy was evaluated through overall contour deviation, occlusal morphology, proximal contact, and emergence profile. Results Relative to the reference (Group 4), AI‐generated crowns (Group 3) demonstrated significantly lower overall contour deviation (438.6 ± 169.4 µm) compared with both software‐generated crowns (Groups 1–2: 679.5–705.1 µm, p < 0.01). Occlusal discrepancies were smaller and contact distributions more comparable to technician‐designed crowns in the AI group, while software‐generated crowns showed higher frequencies of premature contacts. For emergence profile, AI crowns predominantly reproduced concave contours and angles approximating technician‐designed crowns, whereas software groups often produced convex forms. Across all automated groups (1–3), proximal contact adaptation remained less accurate than in technician‐designed crowns. Conclusions Within the limitations of this proof‐of‐concept study, AI‐generated implant‐supported crowns achieved closer approximation to technician designs than software‐generated crowns, particularly in contour, occlusal morphology, and emergence profile.