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Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA.
Kohli, Marc; Alkasab, Tarik; Wang, Ken; Heilbrun, Marta E; Flanders, Adam E; Dreyer, Keith; Kahn, Charles E.
Afiliación
  • Kohli M; Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California. Electronic address: marc.kohli@ucsf.edu.
  • Alkasab T; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Wang K; Department of Radiology, Baltimore VA Medical Center, Baltimore, Maryland.
  • Heilbrun ME; Department of Radiology, Emory University, Atlanta, Georgia.
  • Flanders AE; Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania.
  • Dreyer K; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Kahn CE; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.
J Am Coll Radiol ; 16(10): 1464-1470, 2019 Oct.
Article en En | MEDLINE | ID: mdl-31319078
Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiología / Aplicaciones de la Informática Médica / Inteligencia Artificial Tipo de estudio: Guideline / Prognostic_studies / Sysrev_observational_studies Límite: Humans Idioma: En Revista: J Am Coll Radiol Asunto de la revista: RADIOLOGIA Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiología / Aplicaciones de la Informática Médica / Inteligencia Artificial Tipo de estudio: Guideline / Prognostic_studies / Sysrev_observational_studies Límite: Humans Idioma: En Revista: J Am Coll Radiol Asunto de la revista: RADIOLOGIA Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos