  Generative models such as DALL-E 2 could represent a promising future tool
for image generation, augmentation, and manipulation for artificial
intelligence research in radiology provided that these models have sufficient
medical domain knowledge. Here we show that DALL-E 2 has learned relevant
representations of X-ray images with promising capabilities in terms of
zero-shot text-to-image generation of new images, continuation of an image
beyond its original boundaries, or removal of elements, while pathology
generation or CT, MRI, and ultrasound images are still limited. The use of
generative models for augmenting and generating radiological data thus seems
feasible, even if further fine-tuning and adaptation of these models to the
respective domain is required beforehand.
