Artificial Intelligence versus Conventional Treatment Planning in Oral and Maxillofacial Surgery: A Comparative Review and Practical Framework for Clinical Integration

Background: Treatment planning is central to safe, predictable oral and maxillofacial surgery (OMFS). Conventional planning—clinical examination, 2D radiographs, model surgery and manual virtual surgical planning (VSP)—remains standard. Artificial intelligence (AI) methods (machine learning, deep learning, and automated CAD/CAM pipelines) are increasingly proposed to speed workflows, improve landmarking accuracy, automate segmentation, and provide predictive models for soft-tissue change and postoperative outcomes. Objective: To synthesize current evidence comparing AI-assisted planning with conventional planning workflows in OMFS, highlight clinical benefits and limitations, and propose a practical framework for validated clinical adoption. Methods: A targeted literature search was conducted (PubMed/Medline, PMC, Scopus, Web of Science) focusing on AI applications to OMFS planning (2018–2026). Studies addressing automated cephalometric landmarking, CBCT segmentation, AI-driven virtual surgical planning, outcome prediction, and clinical comparisons of AI vs conventional workflows were prioritized. Key performance metrics extracted included accuracy (landmark error, segmentation Dice/IoU, plan-to-execution deviation), time efficiency, reproducibility, and clinical endpoints where available.