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 jnmt.124.268402
DO 10.2967/jnmt.124.268402
A1 Currie, Geoffrey
A1 Hewis, Johnathan
A1 Hawk, Elizabeth
A1 Kiat, Hosen
A1 Rohren, Eric
YR 2025
UL http://tech.snmjournals.org/content/early/2025/01/15/jnmt.124.268402.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.