Researchers recently evaluated a synthetic pretraining framework that pretrains neural networks using 9000 synthetic magnetic resonance imaging (MRI) projections in less than 10 minutes, addressing the limited availability of medical imaging data for deep learning. The model established a new benchmark across two-dimensional (2D) and three-dimensional (3D) MedMNIST classification tasks, improving performance by up to 17% compared with ImageNet-based pretraining. The framework combines synthetic image generation with self-supervised learning: three class-conditioned denoising diffusion probabilistic models (DDPMs), based on a U-Net architecture, generated 9000 synthetic MRI slices, evenly distributed across axial, coronal, and sagittal planes and representing three diagnostic groups, including healthy controls, mild cognitive impairment, and Alzheimer's disease. Instead of pretraining neural networks to recognize disease categories, the models learned to identify the projection plane of each synthetic image, encouraging the networks to learn general anatomical structures, spatial relationships, and geometric features that could be transferred. The design lays the foundations for a fast and practical alternative that could reduce reliance on the ethical, financial, and regulatory challenges of collecting clinical datasets. The findings were published in Scientific Reports.