Universität zu Köln

dos Santos, D. Pinto, Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D. and Baessler, B. (2019). Medical students' attitude towards artificial intelligence: a multicentre survey. Eur. Radiol., 29 (4). S. 1640 - 1647. NEW YORK: SPRINGER. ISSN 1432-1084

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Abstract

ObjectivesTo assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine.Materials and methodsA 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.ResultsA 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.ConclusionContrary 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 center dot Medical students are aware of the potential applications and implications of AI in radiology and medicine in general.center dot Medical students do not worry that the human radiologist or physician will be replaced.center dot Artificial intelligence should be included in medical training.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code dos Santos, D. Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Giese, D. UNSPECIFIED UNSPECIFIED UNSPECIFIED Brodehl, S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Chon, S. H. UNSPECIFIED UNSPECIFIED UNSPECIFIED Staab, W. UNSPECIFIED UNSPECIFIED UNSPECIFIED Kleinert, R. UNSPECIFIED UNSPECIFIED UNSPECIFIED Maintz, D. UNSPECIFIED UNSPECIFIED UNSPECIFIED Baessler, B. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-152894
DOI:	10.1007/s00330-018-5601-1
Journal or Publication Title:	Eur. Radiol.
Volume:	29
Number:	4
Page Range:	S. 1640 - 1647
Date:	2019
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1432-1084
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BIG DATA; MACHINE; CLASSIFICATION Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/15289