Artificial Intelligence in Digital Implant Planning: A Narrative Review of Current Trends and Future Prospects

Background: Artificial intelligence (AI) has emerged as a streaming technology in dental implant planning that addresses workflow complexity and enhances diagnostic precision across multiple treatment stages. Objective: This narrative review synthesizes current evidence on AI applications throughout the implant planning continuum, examining accuracy metrics, clinical implementation considerations, and future directions. Methods: In 2015-2025, AI applications in implant dentistry, including anatomical segmentation, virtual patient creation, presurgical planning, computer-assisted surgery, prosthetic design, and outcome prediction, were examined. Results: AI demonstrates high accuracy in anatomical segmentation (>90%), edentulous site identification (96% mandibular accuracy, 83% maxillary accuracy), and virtual implant placement (95% clinical acceptability). Compared with human clinicians, deep learning models automate labor-intensive processes, reduce the segmentation time from hours to seconds, and achieve expert-level planning with 2.2-fold faster execution. Compared with conventional guided approaches (1.0 mm), robotic-assisted surgery powered by AI achieves superior positional accuracy (0.5 mm coronal, 0.5 mm apical deviation). AI-powered CAD/CAM systems enhance prosthetic design efficiency and customization. However, significant limitations persist: data heterogeneity reduces generalizability, interference from metal artifacts degrades segmentation, algorithmic opacity undermines clinical trust, and external validation studies remain insufficient. Ethical concerns regarding data privacy, algorithmic bias, and healthcare disparities require careful consideration. Conclusions: AI functions most effectively as a clinical decision support system that augments human expertise rather than replacing clinician judgment. Emerging technologies, including explainable AI frameworks, augmented reality visualization, and hybrid models, promise enhanced clinical integration. Prospective multicenter trials with standardized metrics and transparent validation are essential before widespread clinical deployment. AI-augmented implant planning represents a paradigm shift toward precision, personalized, and predictable implant dentistry.