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Journal of Nuclear Medicine Technology

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Research ArticleBrief Communication

Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging

Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Hosen Kiat and Eric Rohren
Journal of Nuclear Medicine Technology March 2025, 53 (1) 63-67; DOI: https://doi.org/10.2967/jnmt.124.268402
Geoffrey Currie
1Charles Sturt University, Wagga Wagga, New South Wales, Australia;
2Baylor College of Medicine, Houston, Texas;
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Johnathan Hewis
3Charles Sturt University, Port Macquarie, New South Wales, Australia;
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Elizabeth Hawk
1Charles Sturt University, Wagga Wagga, New South Wales, Australia;
4Stanford University, Stanford, California;
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Hosen Kiat
5Cardiac Health Institute, Sydney, New South Wales, Australia;
6College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia; and
7Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
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Eric Rohren
1Charles Sturt University, Wagga Wagga, New South Wales, Australia;
2Baylor College of Medicine, Houston, Texas;
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Abstract

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.

  • generative artificial intelligence
  • image quality
  • medical imaging
  • nuclear medicine

Footnotes

  • Published online Jan. 15, 2025.

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Journal of Nuclear Medicine Technology: 53 (1)
Journal of Nuclear Medicine Technology
Vol. 53, Issue 1
March 1, 2025
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Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging
Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Hosen Kiat, Eric Rohren
Journal of Nuclear Medicine Technology Mar 2025, 53 (1) 63-67; DOI: 10.2967/jnmt.124.268402

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Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging
Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Hosen Kiat, Eric Rohren
Journal of Nuclear Medicine Technology Mar 2025, 53 (1) 63-67; DOI: 10.2967/jnmt.124.268402
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Keywords

  • generative artificial intelligence
  • image quality
  • medical imaging
  • nuclear medicine
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