Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation

Jonathan Herington; Melissa D McCradden; Kathleen Creel; Ronald Boellaard; Elizabeth C Jones; Abhinav K Jha; Arman Rahmim; Peter J H Scott; John J Sunderland; Richard L Wahl; Sven Zuehlsdorff; Babak Saboury

doi:10.2967/jnumed.123.266080

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation

J Nucl Med. 2023 Dec 1;64(12):1848-1854. doi: 10.2967/jnumed.123.266080.

Authors

Affiliations

¹ Department of Health Humanities and Bioethics and Department of Philosophy, University of Rochester, Rochester, New York.
² Department of Bioethics, Hospital for Sick Children, Toronto and Dana Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
³ Department of Philosophy and Religion and Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
⁴ Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands.
⁵ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland.
⁶ Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri.
⁷ Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada.
⁸ Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan.
⁹ Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa.
¹⁰ Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri; and.
¹¹ Siemens Medical Solutions USA, Inc., Hoffman Estates, Illinois.
¹² Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland; [email protected].

Abstract

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

Keywords: AI as medical device; AI ethics; health disparity; socioeconomic determinants of health; software as medical device.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Advisory Committees
Artificial Intelligence*
Data Collection
Humans
Machine Learning*
Molecular Imaging

Grants and funding

R01 EB031051/EB/NIBIB NIH HHS/United States