Trustworthy Artificial Intelligence in Medical Imaging

Navid Hasani¹, Michael A Morris², Arman Rhamim³, Ronald M Summers⁴, Elizabeth Jones⁴, Eliot Siegel⁵, Babak Saboury⁶

Affiliations

¹ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA.
² Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore Country, Baltimore, MD, USA.
³ Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada; Department of Physics, BC cancer Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
⁴ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
⁵ Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada.
⁶ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore Country, Baltimore, MD, USA; Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, 655 W. Baltimore Street, Baltimore, MD 21201, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. Electronic address: babak.saboury@nih.gov.

PMID: 34809860
PMCID: PMC8785402
DOI: 10.1016/j.cpet.2021.09.007

Review

Trustworthy Artificial Intelligence in Medical Imaging

Navid Hasani et al. PET Clin. 2022 Jan.

. 2022 Jan;17(1):1-12.

doi: 10.1016/j.cpet.2021.09.007.

Authors

Navid Hasani¹, Michael A Morris², Arman Rhamim³, Ronald M Summers⁴, Elizabeth Jones⁴, Eliot Siegel⁵, Babak Saboury⁶

Affiliations

¹ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA.
² Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore Country, Baltimore, MD, USA.
³ Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada; Department of Physics, BC cancer Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
⁴ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
⁵ Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada.
⁶ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore Country, Baltimore, MD, USA; Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, 655 W. Baltimore Street, Baltimore, MD 21201, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. Electronic address: babak.saboury@nih.gov.

PMID: 34809860
PMCID: PMC8785402
DOI: 10.1016/j.cpet.2021.09.007

Abstract

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.

Keywords: Ethics of AI; Machine learning; Trustworthiness; Trustworthy artificial intelligence.

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Figures

**Fig. 1**
The 14 core principles and requirements for TAI: the principles are all significant, complement one another, and should be applied and assessed over the entire life cycle of the AI system.

See this image and copyright information in PMC

References

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Trustworthy Artificial Intelligence in Medical Imaging

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Trustworthy Artificial Intelligence in Medical Imaging

Authors

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Abstract

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MeSH terms

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