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Noninterpretive Uses of Artificial Intelligence in Radiology.
Richardson, Michael L; Garwood, Elisabeth R; Lee, Yueh; Li, Matthew D; Lo, Hao S; Nagaraju, Arun; Nguyen, Xuan V; Probyn, Linda; Rajiah, Prabhakar; Sin, Jessica; Wasnik, Ashish P; Xu, Kali.
Afiliação
  • Richardson ML; Department of Radiology, University of Washington, Seattle, Washington. Electronic address: mrich@uw.edu.
  • Garwood ER; Department of Radiology, University of Massachusetts, Worcester, Massachusetts.
  • Lee Y; Department of Radiology, University of North Carolina, Chapel Hill, North Carolina.
  • Li MD; Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Boston, Massachusets.
  • Lo HS; Department of Radiology, University of Washington, Seattle, Washington.
  • Nagaraju A; Department of Radiology, University of Chicago, Chicago, Illinois.
  • Nguyen XV; Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
  • Probyn L; Department of Radiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario.
  • Rajiah P; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Sin J; Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Wasnik AP; Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Xu K; Department of Medicine, Santa Clara Valley Medical Center, Santa Clara, California.
Acad Radiol ; 28(9): 1225-1235, 2021 09.
Article em En | MEDLINE | ID: mdl-32059956
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Idioma: En Ano de publicação: 2021 Tipo de documento: Article