AI in Dental Implant Planning

Dental implant planning uses imaging and clinical information to choose a position, size and orientation that fit the anatomy and intended restoration. AI research in this area often analyzes cone-beam computed tomography (CBCT), for example by segmenting teeth, bone or anatomical structures, or by measuring dimensions for a clinician to inspect. Other studies explore technical assistance for candidate positions. These are separate tasks: performance on segmentation does not demonstrate that a complete treatment plan is safe. Recent reviews report potential for AI assistance, alongside evidence limits. A 2026 review of 28 studies on implant planning rated 20 as low quality under its appraisal method and called for more standardized evaluation before routine clinical integration. A second review describes work on anatomical segmentation and technical planning assistance, with performance varying across tasks and study designs. Many results are image-level or retrospective measures; they do not establish fewer nerve injuries, sinus complications or better long-term implant outcomes. A clinician can use a proposed contour or measurement as a starting point, then inspect the original CBCT and other records, account for restorative goals and patient-specific factors, and decide whether a plan is appropriate. A software display cannot confirm that a structure was segmented correctly or that clinical assumptions are sound. AI may make selected anatomy easier to review, but the surgeon remains responsible for diagnosis, planning and approval.