Medical students' attitude towards artificial intelligence: a multicentre survey.
Artificial intelligence
Education, medical
Radiology
Surveys and questionnaires
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Apr 2019
Apr 2019
Historique:
received:
19
04
2018
accepted:
06
06
2018
revised:
28
05
2018
pubmed:
8
7
2018
medline:
7
5
2019
entrez:
8
7
2018
Statut:
ppublish
Résumé
To assess undergraduate medical students' attitudes towards artificial intelligence (AI) in radiology and medicine. 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.
Identifiants
pubmed: 29980928
doi: 10.1007/s00330-018-5601-1
pii: 10.1007/s00330-018-5601-1
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1640-1646Commentaires et corrections
Type : CommentIn
Références
JAMA. 2016 Dec 13;316(22):2402-2410
pubmed: 27898976
Radiology. 2017 Aug;284(2):574-582
pubmed: 28436741
Nature. 2017 Feb 2;542(7639):115-118
pubmed: 28117445
J Digit Imaging. 2017 Aug;30(4):400-405
pubmed: 28315069
AJR Am J Roentgenol. 2017 Apr;208(4):754-760
pubmed: 28125274
J Am Coll Radiol. 2018 Mar;15(3 Pt B):497-498
pubmed: 29502583
Med Image Anal. 2016 Oct;33:94-97
pubmed: 27481324
J Am Coll Radiol. 2018 Mar;15(3 Pt B):569-576
pubmed: 29502585
Acad Radiol. 2018 Jun;25(6):747-750
pubmed: 29599010