Generative artificial intelligence for synthetic dental data: opportunities, privacy, and validation

Generative artificial intelligence has moved from producing plausible but imperfect dental images to a serious instrument for augmenting scarce datasets, and its trajectory in dentistry can be read in three stages. In the first stage, generative adversarial networks entered dental imaging as a tool for artifact correction and denoising, where the images were a means to a cleaner input rather than an end in themselves. In the second stage, the models turned to synthesis, producing synthetic panoramic, intraoral, and cephalometric images that augmented small datasets and addressed class imbalance, and the dental literature began to evaluate whether synthetic images improve downstream models. In the third, present stage, diffusion models raised the fidelity of synthetic radiographs, and the discussion shifted from whether images can be generated to whether they are private and whether they are valid, two questions the earlier literature largely ignored. The opportunities, augmentation, imbalance correction, rare pathology, and privacy-preserving data sharing, are real, but the privacy of synthetic dental data is not automatic, because generative models can memorize training examples, and the validation of synthetic data, that it is faithful, useful, and safe, is still in its infancy. The review concludes that generative dental data is a promising and rapidly maturing tool whose clinical and regulatory value now depends less on generating better images than on demonstrating that the images are safe to share and trustworthy to train on.