期刊
JOURNAL OF MEDICAL INTERNET RESEARCH
卷 25, 期 -, 页码 -出版社
JMIR PUBLICATIONS, INC
DOI: 10.2196/43110
关键词
DALL-E; creating images from text; image creation; image generation; transformer language model; machine learning; generative model; radiology; x-ray; artificial intelligence; medical imaging; text-to-image; diagnostic imaging
DALL-E 2 shows promising capabilities in generating and manipulating x-ray images, but has limitations in generating images with pathological abnormalities or other medical imaging modalities.
Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, 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, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.
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