Transformer-based models in dentistry: a systematic review
Abstract
Background: Transformer-based architectures have rapidly gained prominence in medical imaging due to their ability to model long-range dependencies and global contextual information more effectively than convolutional neural networks. In dentistry, their applications have expanded across diagnostic, predictive, and generative tasks, yet no comprehensive synthesis has systematically evaluated their performance and clinical relevance. This systematic review provides an up-to-date assessment of Transformer-based models in dental imaging and diagnostics.
Methods: A structured search of Medline, Embase, Web of Science, Scopus, and Cochrane databases was performed for studies published from 2020 to August 2025, following the PRISMA-DTA guidelines for diagnostic test accuracy systematic reviews and a PROSPERO-registered protocol (CRD420251142603). Eligible studies applied Transformer-based architectures to dental clinical tasks and reported quantitative performance outcomes. Two independent reviewers conducted article screening, data extraction, and bias assessment, resolving discrepancies through consensus.
Results: A total of 112 studies met the inclusion criteria. We found 91 hybrid convolutional neural network (CNN)-Transformer architectures and 23 pure Transformer models for dental image segmentation, classification, anomaly detection, and multimodal fusion tasks. In subgroup analyses, hybrid CNN-Transformer models consistently outperformed pure Transformer architectures. A…
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