Objectives: The presence of metallic restorations introduces severe artifacts that compromise the diagnostic accuracy of cone beam computed tomography (CBCT) images. This systematic review aims to evaluate the effectiveness of artificial intelligence (AI) techniques in reducing metal artifacts in dental CBCT images.

Methods: A comprehensive literature search was conducted across 6 databases up until April 2025, using keywords related to AI and CBCT artifact reduction. Studies were selected based on the eligibility criteria, focusing on AI models trained on human CBCT scans targeting dental regions. Data extraction and risk of bias assessment (using the QUADAS-2 tool) were performed independently by several reviewers accordingly.

Results: Ten studies published between 2019 and 2025 met the eligibility criteria. The majority of included studies had a high risk of bias. The most employed deep learning architectures were U-Net, GANs, and transformer-based models. Quantitative metrics like SSIM, PSNR, and RMSE demonstrated consistent improvements in image quality, while qualitative assessments confirmed superior artifact suppression and anatomical detail preservation compared to conventional methods. Transformer-based and physics-informed dual-domain networks performed superior to traditional image-only U-Nets in both quantitative fidelity and visual artifact suppression.

Conclusions: AI-based approaches show promising potential in enhancing CBCT image fidelity by effectively mitigating metal-induced distortions. Despite encouraging results, the broader clinical adoption of such techniques requires standardized evaluation protocols, training models using open-access datasets, and further trials to validate diagnostic impact and generalizability.