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Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside.
Chapiro, Julius; Allen, Bibb; Abajian, Aaron; Wood, Bradford; Kothary, Nishita; Daye, Dania; Bai, Harrison; Sedrakyan, Art; Diamond, Matthew; Simonyan, Vahan; McLennan, Gordon; Abi-Jaoudeh, Nadine; Pua, Bradley.
Afiliação
  • Chapiro J; Division of Vascular and Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut. Electronic address: julius.chapiro@yale.edu.
  • Allen B; Birmingham Radiological Group, Birmingham, Alabama; American College of Radiology, Reston, Virginia.
  • Abajian A; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York.
  • Wood B; Center for Cancer Research, National Institutes of Health, Bethesda, Maryland.
  • Kothary N; Department of Radiology, Stanford Medicine, Palo Alto, California.
  • Daye D; Mass General Imaging, Harvard, Boston, Massachusetts.
  • Bai H; Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, Maryland.
  • Sedrakyan A; Populational Health Sciences, Weill Cornell Medicine, New York City, New York.
  • Diamond M; Digital Health Center of Excellence, Food and Drug Administration, Washington, D.C.
  • Simonyan V; Department of Biochemistry and Molecular Medicine, George Washington School of Medicine and Health Sciences, Washington, D.C.
  • McLennan G; Department of Radiology, University of Colorado Anschutz Medical Center, Denver, Colorado.
  • Abi-Jaoudeh N; Department of Radiology, University of California Irvine, Irvine, California.
  • Pua B; Department of Radiology, Weill Cornell Medicine, New York City, New York.
J Vasc Interv Radiol ; 33(9): 1113-1120, 2022 09.
Article em En | MEDLINE | ID: mdl-35871021
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Radiologia Intervencionista Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: J Vasc Interv Radiol Assunto da revista: ANGIOLOGIA / RADIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Radiologia Intervencionista Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: J Vasc Interv Radiol Assunto da revista: ANGIOLOGIA / RADIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de publicação: Estados Unidos