Generative AI includes a range of machine learning techniques that model data distributions and generate realistic samples. Methods such as flow-based models, diffusion models, variational autoencoders (VAEs), and generative adversarial networks (GANs) have achieved strong results in various fields. In neuroscience imaging, these techniques can enhance data quality and availability by augmenting datasets, completing missing or noisy data, detecting anomalies, and creating realistic simulations for training predictive models. This review explores the growing role of generative AI in neuroscience imaging, focusing on its applications, benefits, and challenges. It highlights how these models can help overcome data shortages, improve visualization methods, and offer new solutions to persistent problems in the field. By summarizing current research and suggesting directions for future work, this paper aims to support researchers and practitioners in using generative AI to advance neuroscience understanding and improve diagnostic and therapeutic outcomes.
AbuEid et al. (Tue,) studied this question.