Medical students' attitude towards artificial intelligence: a multicentre survey
To assess undergraduate medical students’ attitudes towards artificial intelligence (AI) in radiology and medicine.
Materials and methods
A web-based questionnaire was designed using SurveyMonkey, and was sent out to students at three major medical schools. It consisted of various sections aiming to evaluate the students’ prior knowledge of AI in radiology and beyond, as well as their attitude towards AI in radiology specifically and in medicine in general. Respondents’ anonymity was ensured.
A total of 263 students (166 female, 94 male, median age 23 years) responded to the questionnaire. Around 52% were aware of the ongoing discussion about AI in radiology and 68% stated that they were unaware of the technologies involved. Respondents agreed that AI could potentially detect pathologies in radiological examinations (83%) but felt that AI would not be able to establish a definite diagnosis (56%). The majority agreed that AI will revolutionise and improve radiology (77% and 86%), while disagreeing with statements that human radiologists will be replaced (83%). Over two-thirds agreed on the need for AI to be included in medical training (71%). In sub-group analyses male and tech-savvy respondents were more confident on the benefits of AI and less fearful of these technologies.
Contrary to anecdotes published in the media, undergraduate medical students do not worry that AI will replace human radiologists, and are aware of the potential applications and implications of AI on radiology and medicine. Radiology should take the lead in educating students about these emerging technologies.
• Medical students are aware of the potential applications and implications of AI in radiology and medicine in general.
• Medical students do not worry that the human radiologist or physician will be replaced.
• Artificial intelligence should be included in medical training.
KeywordsArtificial intelligence Education, medical Radiology Surveys and questionnaires
Comma Separated Values (a file format)
Magnetic resonance imaging
The authors would like to acknowledge Helen Anna Toder, Franziska Stern, Franziska Inka Meyer, Elisabeth Neuhaus, Jasmin Weindler, Nedim Beste, Sebastian Dern, Omer Reiner and Melanie Treutlein who helped in distributing the questionnaire to the students.
The authors state that this work has not received any funding.
Compliance with ethical standards
The scientific guarantor of this publication is Prof. Dr. David Maintz, University Hospital of Cologne (firstname.lastname@example.org).
Conflict of interest
The authors of this paper declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was not required for this study because this study involved no patients. Participation in the questionnaire was voluntary and had no relation to the students’ curricular activities. Respondents were informed on the nature and purpose of the questionnaire and anonymity was guaranteed.
Institutional Review Board approval was not required because this study involved no patients. Participation in the questionnaire was voluntary and had no relation to the students’ curricular activities. Respondents were informed on the nature and purpose of the questionnaire and anonymity was guaranteed.
• cross-sectional study
• multicentre study
- 5.Hsieh P (2017) Will Computers Be Reading Your Chest X-Ray? Forbes. Available via https://www.forbes.com/sites/paulhsieh/2017/11/27/will-computers-be-reading-your-chest-x-ray/#57bd235514c5 Google Scholar
- 6.Ng A (2017) Tweet on 11.07.2017. Available via https://mobile.twitter.com/andrewyng/status/884810469575344128?lang=de
- 7.Likert R (1932) A technique for the measurement of attitudes. Archives of Psychology. 22:55Google Scholar
- 8.Team R (2016) RStudio: Integrated Development for R. Available via https://www.rstudio.com
- 9.Team RC (2014) R: A language and environment for statistical computing. Available via http://www.r-project.org/
- 12.Tajmir SH, Alkasab TK (2018) Toward Augmented Radiologists: Changes in radiology education in the era of machine learning and Artificial Intelligence. Acad Radiol 25:747-750Google Scholar
- 14.Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118Google Scholar
- 15.Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410Google Scholar