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Artificial Intelligence in Radiology: Resident Recruitment Help or Hindrance?

Published:January 24, 2019DOI:https://doi.org/10.1016/j.acra.2019.01.005
      Radiology should be the medical specialty most primed to incorporate artificial intelligence (AI) into our workflow; however, it seems that many of us are not even sure exactly what that means. This disconnection can lead us to have a negative view of the future of technology-enhanced radiology, which can then discourage medical students from entering the field. Students rely on us to understand how radiology is incorporating new technology and what the future of the field will look like for them, but many of us are ill prepared to teach the younger generation about this, mostly because we ourselves are not sure. The terms AI, machine learning (ML) and deep learning (DL) are often used interchangeably, but these are vastly different concepts with varied applications and potential. The purpose of the study by Gong et al (
      • Gong B.
      • Nugent J.P.
      • Guest W.
      • et al.
      Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a National Survey Study.
      ) accurately captures the gap between the media hype surrounding AI in radiology and the level of understanding, focusing on medical students’ perception. The key difference in this article is the inclusion of additional questions to assess medical students’ knowledge level about AI, which elucidates the gap between perception and reality.
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