Artificial Intelligence, Cone-Beam Computed Tomography (CBCT) and Digital Twins for Predicting Biological and Prosthetic Complications Before Implant Placement: A Narrative Review

Abstract

Despite advances in digital implant planning, biological and prosthetic complications remain difficult to anticipate prior to implant placement because conventional workflows largely rely on static anatomical information. This narrative review examines whether emerging computational technologies, including artificial intelligence, Digital Twins, radiomics and deep learning, can support predictive, patient-specific planning and the preoperative estimation of implant complications. Literature published within the last five years was retrieved from PubMed, Embase and Web of Science and evidence from clinical, computational, experimental, imaging-based and systematic review sources was synthesized. For biological outcomes, models built on integrated patient data perform well, with reported accuracies up to 94.5% and areas under the curve above 0.90 for implant survival, peri-implantitis, marginal bone remodeling and osseointegration; however, external validation is limited and prosthetic complication prediction remains comparatively underdeveloped. Digital Twins provide a dynamic framework that integrates anatomical, functional and biomechanical data to support risk assessment and treatment simulation, while radiomics and deep learning convert CBCT into quantitative bone-quality biomarkers. Current evidence indicates that artificial intelligence cannot yet predict implant failure with certainty before placement, but it can meaningfully enhance risk assessment and advance personalized, predictive implant dentistry.