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

doi:10.2967/jnumed.121.263703

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

J Nucl Med. 2023 Feb;64(2):188-196. doi: 10.2967/jnumed.121.263703. Epub 2022 Dec 15.

Authors

Babak Saboury¹, Tyler Bradshaw², Ronald Boellaard³, Irène Buvat⁴, Joyita Dutta⁵, Mathieu Hatt⁶, Abhinav K Jha⁷, Quanzheng Li⁸, Chi Liu⁹, Helena McMeekin¹⁰, Michael A Morris¹¹, Peter J H Scott¹², Eliot Siegel¹³, John J Sunderland¹⁴, Neeta Pandit-Taskar¹⁵, Richard L Wahl¹⁶, Sven Zuehlsdorff¹⁷, Arman Rahmim¹⁸

Affiliations

¹ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland; [email protected].
² Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.
³ Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
⁴ Institut Curie, Université PSL, INSERM, Université Paris-Saclay, Orsay, France.
⁵ Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, Massachusetts.
⁶ LaTIM, INSERM, UMR 1101, University of Brest, Brest, France.
⁷ Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri.
⁸ Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
⁹ Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut.
¹⁰ Department of Clinical Physics, Barts Health NHS Trust, London, United Kingdom.
¹¹ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland.
¹² Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan.
¹³ Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Maryland.
¹⁴ Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa.
¹⁵ Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
¹⁶ Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri.
¹⁷ Siemens Medical Solutions USA, Inc., Hoffman Estates, Illinois; and.
¹⁸ Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada.

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.

Keywords: artificial intelligence; ecosystem; nuclear medicine; trustworthy.

MeSH terms

Artificial Intelligence*
Ecosystem
Humans
Molecular Imaging
Nuclear Medicine*
Radionuclide Imaging

Abstract

MeSH terms

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