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Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.
Hasani, Navid; Farhadi, Faraz; Morris, Michael A; Nikpanah, Moozhan; Rhamim, Arman; Xu, Yanji; Pariser, Anne; Collins, Michael T; Summers, Ronald M; Jones, Elizabeth; Siegel, Eliot; Saboury, Babak.
Afiliación
  • Hasani N; 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.
  • Farhadi F; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
  • Morris MA; 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.
  • Nikpanah M; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
  • Rhamim A; 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.
  • Xu Y; Office of Rare Diseases Research, National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
  • Pariser A; Office of Rare Diseases Research, National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USA.
  • Collins MT; Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD, USA.
  • Summers RM; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
  • Jones E; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
  • Siegel E; Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, 655 W. Baltimore Street, Baltimore, MD 21201, USA.
  • Saboury B; 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
PET Clin ; 17(1): 13-29, 2022 Jan.
Article en En | MEDLINE | ID: mdl-34809862
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Enfermedades Raras Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: PET Clin Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Enfermedades Raras Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: PET Clin Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos