Education| Volume 29, ISSUE 11, P1748-1756, November 2022

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Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology

  • Samantha M. Santomartino
    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, Maryland
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  • Paul H. Yi
    Address correspondence to: P.H.Y.
    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, Maryland

    Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
    Search for articles by this author
Published:January 29, 2022DOI:

      Rationale and Objectives

      The introduction of AI in radiology has prompted both excitement and hesitation within the field. We performed a systematic review of original studies evaluating the attitudes of radiologists, radiology trainees, and medical students towards AI in radiology.

      Materials and Methods

      We searched PubMed for studies published as of August 24, 2021 for original studies evaluating attitudes of radiologists (attendings and trainees) and medical students towards AI in radiology. We summarized the baseline article characteristics and performed thematic analysis of the questions asked in each study.


      Nineteen studies were included evaluating attitudes across different levels of training (medical students, radiology trainees, and radiology attendings) with representation from nearly every continent. Medical students and radiologists alike favored increased educational initiatives, and displayed interest in learning about and implementing AI solutions themselves, despite reporting of a current gap in formal AI training. There was general optimism about the role of AI in radiology, although radiologists and trainees had greater consensus than medical students.


      Although there is interest in incorporating AI into medical education and optimism among radiologists towards AI, medical students are more divided in their views. We propose that outreach to and AI education for medical students may help improve their attitudes towards the potentially transformative technology of AI for radiology.



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