Artificial intelligence in digital implant dentistry: current applications, clinical translation, and future...

Artificial intelligence (AI) has rapidly expanded across digital implant dentistry, with applications ranging from diagnosis and treatment planning to surgical execution, prosthetic rehabilitation, outcome prediction, and biomechanical optimization. However, the clinical readiness and translational value of many proposed AI systems remain uncertain. This comprehensive state-of-the-art narrative review aims to provide an integrated overview of AI applications throughout the implant workflow while critically evaluating current evidence, methodological limitations, and challenges to clinical implementation. A comprehensive narrative literature search was conducted using PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library, supplemented by Google Scholar and manual reference screening. The review was prepared according to SANRA recommendations. AI has demonstrated promising performance in automated image analysis, implant planning, computer-assisted surgery, prosthetic workflows, implant identification, peri-implant disease detection, predictive modelling, and biomechanical optimization. Nevertheless, most available evidence is derived from retrospective studies, simulation-based investigations, or single-center datasets. Limited external validation, heterogeneity in study design, and insufficient prospective clinical evidence continue to restrict widespread clinical adoption. Furthermore, improvements in technical performance do not necessarily translate into superior patient-centered outcomes. AI has the potential to enhance efficiency, consistency, and clinical decision support throughout the implant workflow. However, current evidence supports its role primarily as an adjunctive tool rather than an autonomous clinical system. Future progress will depend on robust validation, standardized reporting, explainable AI frameworks, and prospective clinical studies demonstrating meaningful improvements in patient outcomes. Clinical Relevance Statement: Artificial intelligence is increasingly embedded in the digital implant workflow, from radiographic analysis and virtual patient construction to computer-assisted surgery, prosthetic design, and outcome prediction. This review helps clinicians distinguish the AI-specific components of these technologies from conventional digital tools, identify which applications are supported by clinical evidence versus those that remain experimental, and understand the current limitations regarding validation, generalizability, and regulatory status. This knowledge supports informed adoption of AI as an adjunctive decision-support tool while maintaining clinician-led verification and patient-centered care.