Artificial Intelligence in Occlusion-Oriented Digital Reconstruction of Maxillofacial Fractures: Current Applications and Translational Challenges

Maxillofacial fractures require reconstruction of a functional craniofacial unit rather than isolated realignment of fractured bone. Stable occlusion, mandibular movement, temporomandibular joint position, facial contour, and fixation-device adaptation should be considered as interdependent treatment targets. Digital workflows incorporating CT or CBCT reconstruction, virtual surgical planning, CAD/CAM, 3-dimensional printing, patient-specific implants or plates, and navigation have improved visualization and surgical transfer. However, comminution, bilateral injury, loss of anatomic references, dental arch disruption, limited mouth opening, metal artifacts, and labor-intensive segmentation still restrict efficiency and reproducibility. This narrative review summarizes artificial intelligence applications across the occlusion-oriented digital reconstruction chain, including image screening, craniofacial and dental segmentation, tooth numbering, model repair, 3-dimensional shape completion, planning assistance, intraoperative registration, and postoperative deviation analysis. The literature was reviewed with emphasis on clinical task, evidence maturity, implementation risk, and translational feasibility. To make the clinical hierarchy explicit, AI applications were classified as near-term supervised clinical support, intermediate translational tools, exploratory research applications, or not ready for routine clinical use. Current evidence supports AI most strongly as supervised decision support for fracture triage, preliminary jaw and tooth segmentation, planning preparation, and postoperative measurement. A representative clinical workflow scenario is included to illustrate how these tasks can be integrated into surgeon-led planning without converting AI outputs into autonomous surgical decisions. Shape completion, automated reduction, fixation design, and real-time intraoperative feedback remain promising early translational applications but require stricter validation before broad clinical deployment. Future translation should prioritize multicenter annotated data sets, external validation, uncertainty reporting, auditable human-in-the-loop workflows, and outcome measures that link radiographic accuracy with occlusal contact, masticatory efficiency, temporomandibular function, patient-reported outcomes, and cost-effectiveness.