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The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education.
Fischetti, Chanel; Bhatter, Param; Frisch, Emily; Sidhu, Amreet; Helmy, Mohammad; Lungren, Matt; Duhaime, Erik.
Affiliation
  • Fischetti C; Brigham and Women's Department of Emergency Medicine, 75 Francis St.Neville House, Boston, MA 02115. Electronic address: cfischetti@bwh.hardvard.edu.
  • Bhatter P; UC Irvine School of Medicine, Irvine, California.
  • Frisch E; UC Irvine School of Medicine, Irvine, California.
  • Sidhu A; Department of Internal Medicine, St. Mary Mercy Hospital, Livonia, Michigan.
  • Helmy M; Department of Radiology, UC Irvine School of Medicine, Irvine, California.
  • Lungren M; Department of Radiology, Stanford Center for Artificial Intelligence in Medicine and Imaging and Stanford University Medical Center, Stanford, California.
  • Duhaime E; Centaur Labs Diagnostics, Inc., Boston, Massachusetts.
Acad Radiol ; 29 Suppl 5: S70-S75, 2022 05.
Article de En | MEDLINE | ID: mdl-34020872
Radiology education is understood to be an important component of medical school and resident training, yet lacks a standardization of instruction. The lack of uniformity in both how radiology is taught and learned has afforded opportunities for new technologies to intervene. Now with the integration of artificial intelligence within medicine, it is likely that the current medical trainee curricula will experience the impact it has to offer both for education and medical practice. In this paper, we seek to investigate the landscape of radiologic education within the current medical trainee curricula, and also to understand how artificial intelligence may potentially impact the current and future radiologic education model.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Radiologie / Internat et résidence Type d'étude: Diagnostic_studies Limites: Humans Langue: En Journal: Acad Radiol Sujet du journal: RADIOLOGIA Année: 2022 Type de document: Article Pays de publication: États-Unis d'Amérique

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Radiologie / Internat et résidence Type d'étude: Diagnostic_studies Limites: Humans Langue: En Journal: Acad Radiol Sujet du journal: RADIOLOGIA Année: 2022 Type de document: Article Pays de publication: États-Unis d'Amérique