Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

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Abstract

Objectives: To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods: An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results: On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions: Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

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Auloge, P., Garnon, J., Robinson, J. M., Dbouk, S., Sibilia, J., Braun, M., … Gangi, A. (2020). Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students? Insights into Imaging, 11(1). https://doi.org/10.1186/s13244-020-00942-y

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