Artificial Intelligence in Dental Implant Treatment Planning: A Systematic Review

Abstract

Background: Artificial intelligence (AI) is increasingly being integrated into digital dentistry and dental implantology, particularly for three-dimensional image analysis, anatomical segmentation, implant-site identification, bone assessment, and computer-assisted treatment planning. Because implant placement is prosthetically and anatomically driven, reliable interpretation of cone-beam computed tomography (CBCT) data is central to reducing surgical and prosthetic complications.

Objective: To systematically synthesize current evidence regarding AI applications in dental implant treatment planning, with emphasis on CBCT-based anatomical identification and segmentation, edentulous-site detection, bone-dimension assessment, technical assistance in planning, and emerging outcome prediction.

Methods: Evidence was synthesized from recent systematic reviews, meta-analyses, scoping reviews, and representative primary studies addressing AI-assisted implant planning. The review framework follows PRISMA 2020 principles and organizes evidence according to population, intervention, comparison, outcomes, AI architecture, imaging modality, and clinical application. Because substantial heterogeneity exists in algorithms, datasets, reference standards, and reported endpoints, quantitative pooling was restricted to outcomes already meta-analyzed in the literature.

Results: Published reviews consistently show high technical performance for several CBCT-based tasks. A 2025 systematic review and meta-analysis reported pooled accuracy of 96% for mandibular and 83% for maxillary edentulous-site identification. A 2026 systematic review of 28 implant-planning studies reported anatomical-segmentation accuracy ranging from 66.4% to 99.1%, with 18 studies addressing segmentation and eight examining technical assistance. A 2026 meta-analysis of U-Net-based mandibular-canal segmentation reported pooled Dice similarity coefficient of 0.84 and substantial heterogeneity. AI-assisted systems can markedly reduce segmentation and planning time, but evidence remains dominated by retrospective, single-centre, and technically focused studies.

Conclusion: AI has substantial potential to improve the speed, consistency, and anatomical precision of dental implant treatment planning, particularly when applied to CBCT segmentation and risk-structure identification. Current evidence does not justify autonomous implant planning. External validation, multicentre prospective studies, standardized reporting, explainability, regulatory oversight, and clinician-in-the-loop workflows are required before routine autonomous use.