An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education.
Merel HuismanErik RanschaertWilliam ParkerDomenico MastrodicasaMartin KociDaniel Pinto de SantosFrancesca CoppolaSergey MorozovMarc ZinsCedric BohynUral KoçJie WuSatyam VeeanDominik FleischmannTim LeinerMartin J WilleminkPublished in: European radiology (2021)
• 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.