Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet!

Cacciamani, Giovanni E; Sanford, Daniel I; Chu, Timothy N; Kaneko, Masatomo; De Castro Abreu, Andre L; Duddalwar, Vinay; Gill, Inderbir S

Cacciamani, Giovanni E; Sanford, Daniel I; Chu, Timothy N; Kaneko, Masatomo; De Castro Abreu, Andre L; Duddalwar, Vinay; Gill, Inderbir S.

Afiliación

Cacciamani GE; USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Sanford DI; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Chu TN; USC/Norris Comprehensive Cancer Center, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
Kaneko M; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
De Castro Abreu AL; Center for Image-Guided Surgery, Focal Therapy and Artificial Intelligence for Prostate Cancer, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Duddalwar V; USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Gill IS; Artificial Intelligence Center at USC Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.

Eur Urol Open Sci ; 48: 14-16, 2023 Feb.

Article en En | MEDLINE | ID: mdl-36588775

Palabras clave

Artificial intelligence; Deep learning; Machine learning; Multiparametric magnetic resonance imaging; Performance; Prostate cancer; Radiology; Radiomics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Eur Urol Open Sci Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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