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Journal of Nuclear Medicine Technology

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Research ArticleSpecial Contribution

Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 1: Preliminary Evaluation

Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk and Eric Rohren
Journal of Nuclear Medicine Technology December 2024, 52 (4) 356-359; DOI: https://doi.org/10.2967/jnmt.124.268332
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; and
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Elizabeth Hawk
4Stanford University, Stanford, California
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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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Journal of Nuclear Medicine Technology: 52 (4)
Journal of Nuclear Medicine Technology
Vol. 52, Issue 4
December 1, 2024
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Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 1: Preliminary Evaluation
Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Eric Rohren
Journal of Nuclear Medicine Technology Dec 2024, 52 (4) 356-359; DOI: 10.2967/jnmt.124.268332

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Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 1: Preliminary Evaluation
Geoffrey Currie, Johnathan Hewis, Elizabeth Hawk, Eric Rohren
Journal of Nuclear Medicine Technology Dec 2024, 52 (4) 356-359; DOI: 10.2967/jnmt.124.268332
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More in this TOC Section

  • Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 2: Analysis of DALL-E 3
  • The Impact of the Coronavirus Disease 2019 Pandemic on the Clinical Environment
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Keywords

  • diversity
  • generative artificial intelligence
  • inclusivity
  • nuclear medicine
  • radiology
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