Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities

Navid Hasani; Faraz Farhadi; Michael A Morris; Moozhan Nikpanah; Arman Rhamim; Yanji Xu; Anne Pariser; Michael T Collins; Ronald M Summers; Elizabeth Jones; Eliot Siegel; Babak Saboury

doi:10.1016/j.cpet.2021.09.009

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities

PET Clin. 2022 Jan;17(1):13-29. doi: 10.1016/j.cpet.2021.09.009.

Authors

Affiliations

¹ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 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, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
³ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 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.
⁴ 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.
⁵ Office of Rare Diseases Research, National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
⁶ Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD, USA.
⁷ Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, 655 W. Baltimore Street, Baltimore, MD 21201, USA.
⁸ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 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, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. Electronic address: babak.saboury@nih.gov.

Abstract

Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.

Keywords: Artificial intelligence; Medical imaging; Positron emission tomography; Rare diseases.

Publication types

Review

MeSH terms

Artificial Intelligence*
Ecosystem
Humans
Positron-Emission Tomography
Radiography
Rare Diseases* / diagnostic imaging

Grants and funding

Z99 CL999999/ImNIH/Intramural NIH HHS/United States