Artificial Intelligence Techniques for Dental Artifact Suppression in Medical Imaging

Dental artifacts are among the most significant diagnostic challenges in computed tomography (CT) and magnetic resonance imaging (MRI), as they degrade image quality and compromise clinical interpretation. This systematic review examined 15 original studies, selected according to the PRISMA flow diagram, with the objective of identifying and evaluating artificial intelligence (AI) algorithms developed for the detection and mitigation of dental artifacts. Performance was assessed with respect to both image quality enhancement, measured through quantitative metrics such as PSNR and SSIM, and diagnostic accuracy. The methodological rigor of the included studies was appraised using the Newcastle–Ottawa Scale. The findings reveal an increasing adoption of deep learning methods—including convolutional neural networks (CNNs), U-Net architectures, and Transformer-based models—alongside traditional metal artifact reduction (MAR) techniques. While these approaches show encouraging results, their performance remains heterogeneous and constrained by the limited size of available datasets. Based on the evidence, we propose a preliminary methodological pipeline to integrate advanced AI techniques for artifact removal and subsequent image classification, with the aim of improving both image quality and diagnostic reliability.