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Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.
Mahmood, Usman; Shukla-Dave, Amita; Chan, Heang-Ping; Drukker, Karen; Samala, Ravi K; Chen, Quan; Vergara, Daniel; Greenspan, Hayit; Petrick, Nicholas; Sahiner, Berkman; Huo, Zhimin; Summers, Ronald M; Cha, Kenny H; Tourassi, Georgia; Deserno, Thomas M; Grizzard, Kevin T; Näppi, Janne J; Yoshida, Hiroyuki; Regge, Daniele; Mazurchuk, Richard; Suzuki, Kenji; Morra, Lia; Huisman, Henkjan; Armato, Samuel G; Hadjiiski, Lubomir.
Afiliación
  • Mahmood U; Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States.
  • Shukla-Dave A; Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States.
  • Chan HP; Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States.
  • Drukker K; Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, United States.
  • Samala RK; Department of Radiology, University of Chicago, Chicago, IL, 60637, United States.
  • Chen Q; Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States.
  • Vergara D; Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, 85054, United States.
  • Greenspan H; Department of Radiology, University of Washington, Seattle, WA, 98195, United States.
  • Petrick N; Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mt Sinai, New York, NY, 10029, United States.
  • Sahiner B; Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States.
  • Huo Z; Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States.
  • Summers RM; Tencent America, Palo Alto, CA, 94306, United States.
  • Cha KH; Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, United States.
  • Tourassi G; Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States.
  • Deserno TM; Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, United States.
  • Grizzard KT; Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Braunschweig, Niedersachsen, 38106, Germany.
  • Näppi JJ; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, 06510, United States.
  • Yoshida H; 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States.
  • Regge D; 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States.
  • Mazurchuk R; Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, 10060, Italy.
  • Suzuki K; Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, 56126, Italy.
  • Morra L; Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, United States.
  • Huisman H; Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
  • Armato SG; Department of Control and Computer Engineering, Politecnico di Torino, Torino, Piemonte, 10129, Italy.
  • Hadjiiski L; Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Gelderland, 6525 GA, Netherlands.
BJR Artif Intell ; 1(1): ubae003, 2024 Jan.
Article en En | MEDLINE | ID: mdl-38476957
ABSTRACT
The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BJR Artif Intell Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BJR Artif Intell Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos