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Research ArticleInvited Perspective

Artificial Intelligence Algorithms Need to Be Explainable—or Do They?

Tyler J. Bradshaw, Melissa D. McCradden, Abhinav K. Jha, Joyita Dutta, Babak Saboury, Eliot L. Siegel and Arman Rahmim
Journal of Nuclear Medicine June 2023, 64 (6) 976-977; DOI: https://doi.org/10.2967/jnumed.122.264949
Tyler J. Bradshaw
1University of Wisconsin–Madison, Madison, Wisconsin;
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Melissa D. McCradden
2Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada;
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Abhinav K. Jha
3Washington University in St. Louis, St. Louis, Missouri;
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Joyita Dutta
4University of Massachusetts Amherst, Amherst, Massachusetts;
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Babak Saboury
5National Institutes of Health, Bethesda, Maryland;
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Eliot L. Siegel
6University of Maryland School of Medicine, Baltimore, Maryland; and
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Arman Rahmim
7University of British Columbia, Vancouver, British Columbia, Canada
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Journal of Nuclear Medicine: 64 (6)
Journal of Nuclear Medicine
Vol. 64, Issue 6
June 1, 2023
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Artificial Intelligence Algorithms Need to Be Explainable—or Do They?
Tyler J. Bradshaw, Melissa D. McCradden, Abhinav K. Jha, Joyita Dutta, Babak Saboury, Eliot L. Siegel, Arman Rahmim
Journal of Nuclear Medicine Jun 2023, 64 (6) 976-977; DOI: 10.2967/jnumed.122.264949

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Artificial Intelligence Algorithms Need to Be Explainable—or Do They?
Tyler J. Bradshaw, Melissa D. McCradden, Abhinav K. Jha, Joyita Dutta, Babak Saboury, Eliot L. Siegel, Arman Rahmim
Journal of Nuclear Medicine Jun 2023, 64 (6) 976-977; DOI: 10.2967/jnumed.122.264949
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  • Article
    • IDENTIFICATION OF CONFOUNDING FACTORS
    • DETECTION OF ALGORITHMIC ERRORS
    • RELIABILITY OF EXPLANATIONS
    • TRANSPARENCY AND TRUSTWORTHINESS
    • PATIENT-CENTERED CARE
    • CONCLUSION
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