Evidence map›Paper›PMID 36522184›Full record

ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2023

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 and 8 more

Abstract read
In one paragraph

Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Socio-Technical Risk-Informed AI-Driven Automation Trustworthiness Evaluation (ST-RATE): Part 1-Conceptualization.Risk analysis : an official publication of the Society for Risk Analysis · 2026
    Article
  3. Article
  4. Krenning score enhances integrated biomarker models for survival prediction in PRRT-treated GEP-NET: a retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
  5. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  6. Article
  7. Review
  8. Dosimetry inJournal of clinical medicine · 2026
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Recommendations for Standardizing Nuclear Medicine Terminology and Data in the Era of Theranostics and Artificial Intelligence.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  15. Review
  16. Article
  17. Article
  18. Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Babak SabouryDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland; sabouryb@nih.gov.
Tyler BradshawDepartment of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.
Ronald BoellaardDepartment of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
Irène BuvatInstitut Curie, Université PSL, INSERM, Université Paris-Saclay, Orsay, France.
Joyita DuttaDepartment of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, Massachusetts.
Mathieu HattLaTIM, INSERM, UMR 1101, University of Brest, Brest, France.
Abhinav K JhaDepartment of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri.
Quanzheng LiDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Chi LiuDepartment of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut.
Helena McMeekinDepartment of Clinical Physics, Barts Health NHS Trust, London, United Kingdom.
Michael A MorrisDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland.
Peter J H ScottDepartment of Radiology, University of Michigan Medical School, Ann Arbor, Michigan.
Eliot SiegelDepartment of Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, Maryland.
John J SunderlandDepartments of Radiology and Physics, University of Iowa, Iowa City, Iowa.
Neeta Pandit-TaskarDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
Richard L WahlMallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri.
Sven ZuehlsdorffSiemens Medical Solutions USA, Inc., Hoffman Estates, Illinois; and.
Arman RahmimDepartments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Longitudinal predictive modeling for tau in Alzheimer's diseaseR01AG072669 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI DUTTA, JOYITA · 2021 to 2025
$2.7M
Super-Resolution Tau PET Imaging for Alzheimer's DiseaseR03AG070750 · NIA · UNIVERSITY OF MASSACHUSETTS LOWELL · PI DUTTA, JOYITA · 2021 to 2022
$304k
NCATS NIH HHS UL1 TR001863NCI NIH HHS P30 CA008748NIA NIH HHS R01 AG072669NIA NIH HHS R03 AG070750
6 · The paper itself

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.

Indexed as

Artificial IntelligenceNuclear MedicineEcosystemHumansMolecular ImagingRadionuclide Imagingartificial intelligenceecosystemnuclear medicinetrustworthy

Identifiers

PMID36522184
PMCPMC9902852

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.