Highlights
- •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.
Abstract
Purpose
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.
Methods
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.
Results
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.
Conclusion
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.
Keywords
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Article info
Publication history
Published online: October 02, 2021
Accepted:
September 17,
2021
Received:
April 19,
2021
Identification
Copyright
© 2021 Elsevier Inc. All rights reserved.
