Progress in the development of deep learning tools for dental imaging is constrained by limited access to real-world datasets due to privacy concerns, class imbalance, and data scarcity. This narrative review focuses on the use of synthetic data as a potential solution to these challenges.

The review addresses both technical, clinical, and ethical/regulatory aspects, and was drafted by a multidisciplinary team. Each subsection was assigned to at least two contributors, with two central members overseeing the entire process. Relevant studies were identified through electronic searches in PubMed, Scopus, Embase, Google Scholar, Web of Science, and IEEE Xplore, supplemented by conference papers and book chapters. For the subsection on clinical applications, publications in the domain of dentistry and oral health focused on fully synthetic image generation were included; studies on image translation or other image processing tasks were excluded.

Synthetic imaging data can be generated using generative adversarial networks, variational autoencoders, and denoising diffusion probabilistic models. Synthetic imaging can complement real-world data by mitigating class imbalance, augmenting scarce datasets, and enabling diverse, realistic representations of rare conditions and anatomical variations. It holds promise for diagnostics, education, and multimodal integration across imaging modalities. Studies on dental image synthesis remain scarce, and comprehensive evidence regarding the impact of data augmentation using synthetic images is lacking. Key challenges persist, including ensuring anatomical fidelity and minimizing artifacts. Future emphasis should be on interdisciplinary collaboration, standardized generation workflows, open-source tools, robust strategies for synthetic data integration, and clear regulatory guidance.

Synthetic imaging can help overcome data scarcity and class imbalance in dental artificial intelligence (AI), leading to more robust and generalizable AI models.