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Research ArticleThe State of the Art
Open Access

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Babak Saboury, Tyler Bradshaw, Ronald Boellaard, Irène Buvat, Joyita Dutta, Mathieu Hatt, Abhinav K. Jha, Quanzheng Li, Chi Liu, Helena McMeekin, Michael A. Morris, Peter J.H. Scott, Eliot Siegel, John J. Sunderland, Neeta Pandit-Taskar, Richard L. Wahl, Sven Zuehlsdorff and Arman Rahmim
Journal of Nuclear Medicine February 2023, 64 (2) 188-196; DOI: https://doi.org/10.2967/jnumed.121.263703
Babak Saboury
1Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland;
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Tyler Bradshaw
2Department of Radiology, University of Wisconsin–Madison, Madison, Wisconsin;
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Ronald Boellaard
3Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands;
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Irène Buvat
4Institut Curie, Université PSL, INSERM, Université Paris–Saclay, Orsay, France;
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Joyita Dutta
5Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, Massachusetts;
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Mathieu Hatt
6LaTIM, INSERM, UMR 1101, University of Brest, Brest, France;
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Abhinav K. Jha
7Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri;
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Quanzheng Li
8Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts;
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Chi Liu
9Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut;
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Helena McMeekin
10Department of Clinical Physics, Barts Health NHS Trust, London, United Kingdom;
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Michael A. Morris
1Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland;
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Peter J.H. Scott
11Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan;
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Eliot Siegel
12Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Maryland;
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John J. Sunderland
13Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa;
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Neeta Pandit-Taskar
14Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York;
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Richard L. Wahl
15Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri;
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Sven Zuehlsdorff
16Siemens Medical Solutions USA, Inc., Hoffman Estates, Illinois; and
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Arman Rahmim
17Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
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Abstract

Trustworthiness is a core tenet of medicine. The patient–physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.

  • artificial intelligence
  • trustworthy
  • nuclear medicine
  • ecosystem

Footnotes

  • Published online Dec. 15, 2022.

  • © 2023 by the Society of Nuclear Medicine and Molecular Imaging.

Immediate Open Access: Creative Commons Attribution 4.0 International License (CC BY) allows users to share and adapt with attribution, excluding materials credited to previous publications. License: https://creativecommons.org/licenses/by/4.0/. Details: http://jnm.snmjournals.org/site/misc/permission.xhtml.

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Journal of Nuclear Medicine: 64 (2)
Journal of Nuclear Medicine
Vol. 64, Issue 2
February 1, 2023
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Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem
Babak Saboury, Tyler Bradshaw, Ronald Boellaard, Irène Buvat, Joyita Dutta, Mathieu Hatt, Abhinav K. Jha, Quanzheng Li, Chi Liu, Helena McMeekin, Michael A. Morris, Peter J.H. Scott, Eliot Siegel, John J. Sunderland, Neeta Pandit-Taskar, Richard L. Wahl, Sven Zuehlsdorff, Arman Rahmim
Journal of Nuclear Medicine Feb 2023, 64 (2) 188-196; DOI: 10.2967/jnumed.121.263703

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Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem
Babak Saboury, Tyler Bradshaw, Ronald Boellaard, Irène Buvat, Joyita Dutta, Mathieu Hatt, Abhinav K. Jha, Quanzheng Li, Chi Liu, Helena McMeekin, Michael A. Morris, Peter J.H. Scott, Eliot Siegel, John J. Sunderland, Neeta Pandit-Taskar, Richard L. Wahl, Sven Zuehlsdorff, Arman Rahmim
Journal of Nuclear Medicine Feb 2023, 64 (2) 188-196; DOI: 10.2967/jnumed.121.263703
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  • Article
    • Abstract
    • OPPORTUNITIES
    • AI ECOSYSTEM
    • CHALLENGES FOR DEVELOPMENT, VALIDATION, DEPLOYMENT, AND IMPLEMENTATION
    • TRUST AND TRUSTWORTHINESS
    • RESPONSIBILITIES: TOWARD TRUSTWORTHY AI
    • STRATEGIES FOR SUCCESS
    • CONCLUSION
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Keywords

  • artificial intelligence
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  • ecosystem
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