What Does DALL-E 2 Know About Radiology?

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-toimage 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. DALL-E 2 is a novel deep learning model for text-to-image generation, first introduced by OpenAI in April 2022 [Ramesh et al., 2022]. The model has recently gained widespread public interest due to its ability to create photorealistic images solely from short written inputs [Kather et al., 2022][Conwell and Ullman, 2022][Marcus et al., 2022].