Impact of artificial intelligence on US medical students' choice of radiology

  • Kristen Reeder
    MD Program, Quinnipiac University Frank H Netter MD School of Medicine, 370 Bassett Rd, North Haven, CT 06473, USA
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  • Hwan Lee
    Corresponding author at: Department of Radiology, 3400 Spruce Street, Philadelphia, PA 19104, USA.
    Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce Street, Philadelphia, PA 19104, USA
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      • AI has a significantly negative impact on US medical students' choice of radiology.
      • AI may deter one-sixth of students from choosing radiology as their top specialty.
      • Students obtain negative opinions on AI in radiology from the medical community.



      International student surveys have shown significant anxiety about pursuing radiology as a career due to artificial intelligence (AI). For a counterpart study in the US, we examined the impact of AI on US medical students' choice of radiology as a career, and how such impact is influenced by students' opinions on and exposures to AI and radiology.


      Students across 32 US medical schools participated in an anonymous online survey. The respondents' radiology ranking with and without AI were compared. Among those considering radiology within their top 3 choices, change in radiology ranking due to AI was statistically examined for association with baseline characteristics, subjective opinions, and prior exposures.


      AI significantly lowered students' preference for ranking radiology (P < .001). One-sixth of students who would have chosen radiology as the first choice did not do so because of AI, and approximately half of those considering radiology within their top 3 choices remained concerned about AI. Ranking radiology lower due to AI was associated with greater concerns about AI (P < .001), less perceived understanding of radiology (P = .02), predicting a decrease in job opportunities (P < .001), and exposure to AI through medical students/family (P = .03) as well as through radiology attendings and residents (P = .03). Education on AI during radiology rotations, followed by pre-clinical lectures, was the most preferred way to learn about AI.


      AI has a significantly negative impact on US medical students' choice of radiology as a career, a phenomenon influenced by both individual concerns and exposure to AI from the medical community.


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        • Jalal S.
        • Nicolaou S.
        • Parker W.
        Artificial intelligence, radiology, and the way forward.
        Can Assoc Radiol J. 2019; 70: 10-12
        • Syed A.B.
        • Zoga A.C.
        Artificial intelligence in radiology: current technology and future directions.
        Semin Musculoskelet Radiol. 2018; 22: 540-545
        • Pesapane F.
        • Codari M.
        • Sardanelli F.
        Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.
        Eur Radiol Exp. 2018; 2: 35
        • Bluemke D.A.
        Radiology in 2018: are you working with AI or being replaced by AI?.
        Radiology. 2018; 287: 365-366
        • Recht M.
        • Bryan R.N.
        Artificial intelligence: threat or boon to radiologists?.
        J Am Coll Radiol. 2017; 14: 1476-1480
        • Gong B.
        • Nugent J.P.
        • Guest W.
        • Parker W.
        • Chang P.J.
        • Khosa F.
        • et al.
        Influence of artificial intelligence on Canadian medical Students' preference for radiology specialty: anational survey study.
        Acad Radiol. 2019; 26: 566-577
        • Pinto Dos Santos D.
        • Giese D.
        • Brodehl S.
        • Chon S.H.
        • Staab W.
        • Kleinert R.
        • et al.
        Medical students' attitude towards artificial intelligence: a multicentre survey.
        Eur Radiol. 2019; 29: 1640-1646
        • van Hoek J.
        • Huber A.
        • Leichtle A.
        • Harma K.
        • Hilt D.
        • von Tengg-Kobligk H.
        • et al.
        A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.
        Eur J Radiol. 2019; 121108742
        • Park C.J.
        • Yi P.H.
        • Siegel E.L.
        Medical student perspectives on the impact of artificial intelligence on the practice of medicine.
        Curr Probl Diagn Radiol. 2021; 50: 614-619
        • Arleo E.K.
        • Bluth E.
        • Francavilla M.
        • Straus C.M.
        • Reddy S.
        • Recht M.
        Surveying fourth-year medical students regarding the choice of diagnostic radiology as a specialty.
        J Am Coll Radiol. 2016; 13: 188-195
        • Yen A.J.
        • Webb E.M.
        • Jordan E.J.
        • Kallianos K.
        • Naeger D.M.
        The stability of factors influencing the choice of medical specialty among medical students and postgraduate radiology trainees.
        J Am Coll Radiol. 2018; 15: 886-891
        • Lee H.
        • Kim D.H.
        • Hong P.P.
        Radiology clerkship requirements in Canada and the United States: current state and impact on residency application.
        J Am Coll Radiol. 2020; 17: 515-522
        • Grayev A.
        Artificial intelligence in radiology: resident recruitment help or hindrance?.
        Acad Radiol. 2019; 26: 699-700
        • National Resident Matching Program
        Results and data: 2020 main residency Match®.
        National Resident Matching Program, Washington, DC2020
        • Sit C.
        • Srinivasan R.
        • Amlani A.
        • Muthuswamy K.
        • Azam A.
        • Monzon L.
        • et al.
        Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey.
        Insights Imaging. 2020; 11: 14
        • McCoy L.G.
        • Nagaraj S.
        • Morgado F.
        • Harish V.
        • Das S.
        • Celi L.A.
        What do medical students actually need to know about artificial intelligence?.
        NPJ Digit Med. 2020; 3: 86