PT  - JOURNAL ARTICLE
AU  - Currie, Geoffrey
AU  - Hewis, Johnathan
AU  - Hawk, Elizabeth
AU  - Kiat, Hosen
AU  - Rohren, Eric
TI  - Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging
AID  - 10.2967/jnmt.124.268402
DP  - 2025 Mar 01
TA  - Journal of Nuclear Medicine Technology
PG  - 63--67
VI  - 53
IP  - 1
4099  - http://tech.snmjournals.org/content/53/1/63.short
4100  - http://tech.snmjournals.org/content/53/1/63.full
SO  - J. Nucl. Med. Technol.2025 Mar 01; 53
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.