Background: Digital workflows have transformed orthognathic surgery by enabling three-dimensional virtual surgical planning (VSP) and, more recently, artificial-intelligence-based predictive modeling. While skeletal accuracy has improved substantially, the clinical value of these tools increasingly depends on their ability to predict soft-tissue outcomes and support complex treatment decisions. Methods: This narrative review synthesizes evidence from 20 peer-reviewed studies, including systematic reviews, clinical cohorts, and machine-learning validations, to evaluate (i) the accuracy of digital patient modeling and VSP transfer, (ii) the performance of predictive models for postoperative outcomes and diagnostic classification, and (iii) associated ethical and governance considerations. Results: Automated craniofacial and dental modeling achieves submillimetric to low-millimetric accuracy, with landmark localization errors typically ≤2 mm and dental landmark errors near 0.4 mm. Clinical cohorts demonstrate that VSP transfers to surgery with mean translational deviations well below 2 mm in all planes. Predictive models estimate soft-tissue and aesthetic outcomes within clinically meaningful error margins and classify the need for orthognathic surgery with AUC values up to ~0.9 in high-risk malocclusion groups. Conclusions: Digital planning in orthognathic surgery has evolved into a precision-oriented, data-driven framework. When combined with predictive modeling and appropriate ethical governance, these technologies can enhance accuracy, personalize treatment strategies, and reduce diagnostic uncertainty, particularly in borderline and asymmetric cases.
Woźniak et al. (Mon,) studied this question.