RT Journal Article
SR Electronic
T1 Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging
JF Journal of Nuclear Medicine Technology
JO J. Nucl. Med. Technol.
FD Society of Nuclear Medicine
SP 63
OP 67
DO 10.2967/jnmt.124.268402
VO 53
IS 1
A1 Currie, Geoffrey
A1 Hewis, Johnathan
A1 Hawk, Elizabeth
A1 Kiat, Hosen
A1 Rohren, Eric
YR 2025
UL http://tech.snmjournals.org/content/53/1/63.abstract
AB The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourney has been touted with popular hype about the transformative potential in health care. This hype-driven, rapid assimilation comes with few professional guidelines and without regulatory oversight. Despite documented limitations, text-to-image generative AI creations have permeated nuclear medicine and medical imaging. Given the representation of medical imaging professions and potential dangers in misrepresentation and errors from both a reputation and community harm perspective, critical quality assurance of text-to-image generative AI creations is required. Here, tools for evaluating the quality and fitness for purpose of generative AI images in nuclear medicine and imaging are discussed. Generative AI text-to-image creation suffers quality limitations that are generally prohibitive of mainstream use in nuclear medicine and medical imaging. Text-to-image generative AI diffusion models should be used within a framework of critical quality assurance for quality and accuracy.