AI Summary & Key Takeaways
AI InsightFuture research should prioritize multimodal diagnostic frameworks, federated learning strategies, explainable AI techniques, and integration with digital and robotic surgical systems.
📌 3 Key Takeaways
Abstract
Artificial intelligence (AI) has been increasingly applied to diagnostic imaging in dentistry, particularly for the automated identification and classification of dental implants. The growing diversity of implant systems and the frequent unavailability of historical clinical records complicate accurate implant recognition and highlight the need for reliable imaging-based diagnostic support systems. This narrative review aims to summarize and critically evaluate AI-based diagnostic approaches for dental implant identification using panoramic radiographs and cone-beam computed tomography (CBCT) published between 2020 and 2025, focusing on model architectures, diagnostic performance, clinical applicability, and emerging trends. A comprehensive literature search was conducted in PubMed and Google Scholar using combinations of keywords related to dental implant identification, deep learning, panoramic radiography and CBCT. Twenty-one panoramic and three CBCT-based studies that met the inclusion criteria were analyzed. The extracted data included imaging modality, AI architecture, dataset characteristics, and reported performance metrics. Panoramic radiography–based studies predominantly employed convolutional neural networks (CNNs), YOLO-based object detection frameworks, attention-based neural networks, ensemble learning strategies, and multi-task deep learning architectures. Reported implant identification accuracies ranged from approximately 70% in early YOLO-based pilot studies to 95–98% in large, multicenter CNN-based models. Several models have been extended beyond implant brand classification to perform automated implant localization, angulation assessment, treatment stage classification, and detection of implant-related complications. CBCT-based studies mainly utilized three-dimensional convolutional neural networks, including 3D U-Net–derived and progressive multi-task architectures, achieving high segmentation performance with Dice coefficients exceeding 0.90 and enabling detailed volumetric analysis of implant morphology. AI-driven implant identification systems demonstrate strong diagnostic performance and represent promising tools for imaging-based clinical decision support, particularly in revision surgery planning and peri-implant assessment. However, limitations related to dataset heterogeneity, external validation, and model interpretability remain. Future research should prioritize multimodal diagnostic frameworks, federated learning strategies, explainable AI techniques, and integration with digital and robotic surgical systems.