• AI negatively impact radiology as a career choice for US medical students.
  • AI discouraged one-sixth of students from selecting radiology as a top speciality choice.
  • The medical community pass on negative opinions on Radiology AIs to students.

 

Incorporating artificial intelligence (AI) into the radiology workflow is an active research area, where innovation focuses on providing decision support to increase radiologist efficiency and decrease workload burden.  Although a boon to practising radiologists, incorporating AI into the radiological workflow also bears on medical students considering a future career in radiology. To determine how radiological AIs might affect the recruitment for radiology residencies, an online survey was distributed to 463 medical students attending one of 32 US accredited medical schools (Reader & Lee 2022).

 

One-sixth of the students, who would have first chosen to specialize in radiology, did not because of AI concerns.  Half of the 188 students who considered radiology within their top three career choices remained worried about AI. Avoidance of radiology as a career was associated with:

  1. Lower perceived understanding of radiology
  2. Fear of decreased job opportunities
  3. Previous AI exposure through:
    • Medical students/family
    • Radiology attendings and residents

 

The authors summed up: ‘AI is deterring US medical students from applying to radiology, fuelled by both individual concerns and exposure to AI from the medical community.’

 

These survey results complement earlier studies showing concern towards radiology and AI. The percentage of medical students with concern towards AI this study (53%) echoes other surveys in Canada (56%), United Kingdom (49%), and Germany (38%) Gong B. et al. 2019; Pinto Dos Santos D. et al. 2019; Sit C. et al. 2020). However, the degree of concern differed. For example, the Canadian study (Gong et al 2019) revealed that approximately one-third of medical students thought AI would fully replace radiologists, whereas 83% in the German survey felt the opposite (Sit C. et al. 2020).

 

To learn about AI use in their speciality choice, survey respondents preferred curricular integration over self-directed learning. Given that clinical radiology rotations are uncommon as a requirement in US medical schools, the study’s authors felt that incorporating AI education into pre-clinical lectures may be a more effective way to teach AI. The survey respondents echoed these sentiments.

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References:

Reeder K. & Lee H. (2022) Impact of artificial intelligence on US medical students’ choice of radiology. Clinical Imaging, 81, 67-71.


Gong B. et al. (2019) Influence of artificial intelligence on Canadian medical Students’ preference for radiology specialty: a national survey study. 26: 566-577.


Pinto Dos Santos D. et al. (2019) Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 29: 1640-1646.


Sit C. et al. (2020) Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 11: 14.




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Artificial Intelligence, Artificial Intelligence in diagnostic radiology, Artificial intelligence in imaging, radiology resident training, radiology residents, Evolving Role of Artificial Intelligence in Radiological Imaging An online survey distributed to US medical students shows that AI deters medical students from choosing radiology residencies.