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An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education
130
Zitationen
16
Autoren
2021
Jahr
Abstract
• There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.
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Autoren
Institutionen
- University Medical Center Utrecht(NL)
- Elisabeth-TweeSteden Ziekenhuis(NL)
- University of British Columbia(CA)
- Stanford University(US)
- University Hospital in Motol(CZ)
- University Hospital Cologne(DE)
- Azienda USL di Bologna(IT)
- Hôpital Saint Joseph(FR)
- Universitair Ziekenhuis Leuven(BE)
- Southwestern Medical Center(US)
- Southwestern Medical Center
- The University of Texas Southwestern Medical Center(US)