Abstract
Objectives
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.
Results
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.
Conclusion
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.
Key Points
• 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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Abbreviations
- AI:
-
Artificial intelligence
- CSV:
-
Comma Separated Values (a file format)
- IQR:
-
Interquartile range
- MRI:
-
Magnetic resonance imaging
References
Carlos RC, Kahn CE, Halabi S (2018) Data Science: Big Data, Machine Learning, and Artificial Intelligence. J Am Coll Radiol 15:497–498
Kohli M, Prevedello LM, Filice RW, Geis JR (2017) Implementing Machine Learning in Radiology Practice and Research. AJR Am J Roentgenol 208:754–760
Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K (2017) Toolkits and Libraries for Deep Learning. J Digit Imaging 30:400–405
Lakhani P, Sundaram B (2017) Deep Learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284:574–582
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
Ng A (2017) Tweet on 11.07.2017. Available via https://mobile.twitter.com/andrewyng/status/884810469575344128?lang=de
Likert R (1932) A technique for the measurement of attitudes. Archives of Psychology. 22:55
Team R (2016) RStudio: Integrated Development for R. Available via https://www.rstudio.com
Team RC (2014) R: A language and environment for statistical computing. Available via http://www.r-project.org/
Wickham H, Chang W (2009) ggplot2: An implementation of the Grammar of Graphics. Available via http://ggplot2.tidyverse.org/
Syeda-Mahmood T (2018) Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology. J Am Coll Radiol 15:569–576
Tajmir SH, Alkasab TK (2018) Toward Augmented Radiologists: Changes in radiology education in the era of machine learning and Artificial Intelligence. Acad Radiol 25:747-750
de Bruijne M (2016) Machine learning approaches in medical image analysis: From detection to diagnosis. Med Image Anal 33:94–97
Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118
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 316:2402–2410
Acknowledgements
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.
Funding
The authors state that this work has not received any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Guarantor
The scientific guarantor of this publication is Prof. Dr. David Maintz, University Hospital of Cologne (david.maintz@uk-koeln.de).
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.
Informed consent
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.
Ethical approval
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.
Methodology
• prospective
• cross-sectional study
• multicentre study
Rights and permissions
About this article
Cite this article
Pinto dos Santos, D., Giese, D., Brodehl, S. et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol 29, 1640–1646 (2019). https://doi.org/10.1007/s00330-018-5601-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-018-5601-1