Online citations, reference lists, and bibliographies.
← Back to Search

Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey

D. Pinto dos Santos, D. Giese, S. Brodehl, S. Chon, W. Staab, R. Kleinert, D. Maintz, B. Baessler
Published 2018 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy Visualize in Litmaps
Share
Reduce the time it takes to create your bibliography by a factor of 10 by using the world’s favourite reference manager
Time to take this seriously.
Get Citationsy
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• 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 paper references
A technique for the measurement of attitudes
R. Likert (1932)
R Likert (1932)
10.1007/978-0-387-98141-3
ggplot2 - Elegant Graphics for Data Analysis
(2009)
R: A language and environment for statistical computing.
(2014)
An Implementation of the Grammar of Graphics
H. Wickham (2015)
10.1016/j.media.2016.06.032
Machine learning approaches in medical image analysis: From detection to diagnosis.
Marleen de Bruijne (2016)
10.1001/jama.2016.17216
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
Varun Gulshan (2016)
10.1016/j.media.2016.06.032
Machine learning approaches in medical image analysis: From detection to diagnosis
Marleen de Bruijne (2016)
RStudio: Integrated Development for R. Available via https://www.rstudio.com
R Team (2016)
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410
V Gulshan (2016)
10.1007/s10278-017-9965-6
Toolkits and Libraries for Deep Learning
B. Erickson (2017)
10.1148/radiol.2017162326
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.
P. Lakhani (2017)
10.1038/nature21056
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva (2017)
10.2214/AJR.16.17224
Implementing Machine Learning in Radiology Practice and Research.
M. Kohli (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
P Hsieh (2017)
P Hsieh (2017)
Will Computers Be Reading Your Chest X - Ray ? Forbes
R Likert (2017)
10.1016/j.acra.2018.03.007
Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.
Shahein H. Tajmir (2018)
10.1016/j.jacr.2018.01.029
Data Science: Big Data, Machine Learning, and Artificial Intelligence.
R. Carlos (2018)
10.1016/j.jacr.2018.01.028
Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.
T. Syeda-Mahmood (2018)
automated classification of pulmonary tuberculosis by using convolutional neural networks
P Lakhani (2019)



This paper is referenced by
10.1007/s00259-021-05558-y
Integrating artificial intelligence into radiology practice: undergraduate students’ perspective
Arosh S. Perera Molligoda Arachchige (2021)
10.1016/j.jaapos.2021.01.011
Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology.
N. Valikodath (2021)
10.1177/0739456X20984529
Living with Autonomy: Public Perceptions of an AI-Mediated Future
Eva Kassens-Noor (2021)
10.1186/s12913-021-07044-5
Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey
Bo Zheng (2021)
10.3389/fdgth.2021.739327
Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers
(2021)
10.1038/s41587-021-00846-2
Medical students need artificial intelligence and machine learning training
A. Pucchio (2021)
10.3389/frsc.2021.723475
Choosing Ethics Over Morals: A Possible Determinant to Embracing Artificial Intelligence in Future Urban Mobility
E. Kassens-Noor (2021)
10.19177/JRD.V9E120216-13
SHOULD ARTIFICIAL INTELLIGENCE INTEGRATE WITH DENTAL EDUCATION? AN ASSESSMENT THROUGH THE DENTOMAXILLOFACIAL RADIOLOGY PERSPECTIVE
C. Buyuk (2021)
10.2196/preprints.34678
The perspective of IT decision makers on factors influencing adoption and implementation of AI-technologies in German Hospitals: Descriptive Analysis (Preprint)
(2021)
10.20524/aog.2021.0606
Artificial intelligence and capsule endoscopy: unravelling the future
M. Mascarenhas (2021)
10.1007/s00330-021-07782-4
An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education
M. Huisman (2021)
10.1111/tct.13391
Teaching data science to medical trainees
Ryan Wee (2021)
10.1038/s41598-021-94178-5
Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia
J. Scheetz (2021)
10.3934/neuroscience.2021025
Neurosurgery and artificial intelligence
Mohammad Mofatteh (2021)
10.2139/ssrn.3772076
My Boss the Computer: A Bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward the Non-Human Resource Management
Peter Mantello (2021)
10.3390/healthcare9070834
Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology
Magbool Alelyani (2021)
10.1002/ase.2060
Assessing Anatomy Education: A Perspective from Design
Mark Roxburgh (2021)
10.1136/bmjhci-2020-100293
Women’s attitudes to the use of AI image readers: a case study from a national breast screening programme
N. Lennox-Chhugani (2021)
10.1016/j.ejmp.2021.02.007
Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools.
O. Díaz (2021)
10.2196/preprints.35223
Artificial Intelligence (AI) education for the health workforce: An expert survey of approaches and needs (Preprint)
(2021)
10.1007/s00146-021-01290-1
Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace
Peter Mantello (2021)
10.1007/s00330-020-07621-y
Training opportunities of artificial intelligence (AI) in radiology: a systematic review
Floor Schuur (2021)
10.1038/s41598-021-84698-5
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
J. Scheetz (2021)
10.1016/j.artmed.2021.102190
A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence.
Andreia Martinho (2021)
10.1177/23821205211024078
Are We Ready to Integrate Artificial Intelligence Literacy into Medical School Curriculum: Students and Faculty Survey
Elena Wood (2021)
10.3389/fpubh.2021.623088
Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University
C. Blease (2021)
Artificial Intelligence for Health Professions Educators.indd
K. Lomis (2021)
10.1177/14757257211037149
Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?
Sabrina Gado (2021)
10.1007/s00247-021-05195-5
Artificial intelligence in paediatric radiology: international survey of health care professionals’ opinions
S. Shelmerdine (2021)
10.1016/j.procs.2021.03.030
Fog-cloud assisted framework for Heterogeneous Internet of Healthcare Things
Rashmi Chudhary (2021)
10.1007/s00330-021-08214-z
Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review
Ling Yang (2021)
10.1016/j.acra.2021.08.020
Radiology Stereotypes, Application Barriers, and Hospital Integration: A Mixed-methods Study of Medical Student Perceptions of Radiology.
L. Grimm (2021)
See more
Semantic Scholar Logo Some data provided by SemanticScholar