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

Journal: Journal of nuclear medicine : official publication, Society of Nuclear Medicine
PMID: 36522184

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.

Authors

  • Babak Saboury
    IBM Research, Almaden, San Jose, California.
  • Tyler Bradshaw
  • Ronald Boellaard
    Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands.
  • Irène Buvat
    Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Joyita Dutta
  • Mathieu Hatt
    LaTIM, INSERM, UMR 1101, Brest 29609, France.
  • Abhinav K Jha
    Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States of America.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Chi Liu
  • Helena McMeekin
    Department of Clinical Physics, Barts Health NHS Trust, London, United Kingdom.
  • Michael A Morris
    Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Institute for Data Science, Department of Diagnostic Radiology and Nuclear Medicine - University of Miami Miller School of Medicine, Miami, FL, USA.
  • Peter J H Scott
    Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: pjhscott@umich.edu.
  • Eliot Siegel
    University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, 504 E. Fort Ave Baltimore, MD 21230.
  • John J Sunderland
    Department of Radiology, The University of Iowa, Iowa City, IA, 52242, USA.
  • Neeta Pandit-Taskar
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Richard L Wahl
    Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri.
  • Sven Zuehlsdorff
    Siemens Medical Solutions USA, Inc., Knoxville, Tennessee.
  • Arman Rahmim

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