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Cited by 106Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

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Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives

Open access
Ejaz, Hamza, McGrath, Hari, Wong, Brian LH, Guise, Andrew, Vercauteren, Tom, Shapey, Jonathan·2022·DIGITAL HEALTH
DOI 10.1177/20552076221089099PMC PMC9067043
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Abstract

Objective: Medical students, as clinicians and healthcare leaders of the future, are key stakeholders in the clinical roll-out of artificial intelligence-driven technologies. The authors aim to provide the first report on the state of artificial intelligence in medical education globally by exploring the perspectives of medical students. Methods: The authors carried out a mixed-methods study of focus groups and surveys with 128 medical students from 48 countries. The study explored knowledge around artificial intelligence as well as what students wished to learn about artificial intelligence and how they wished to learn this. A combined qualitative and quantitative analysis was used. Results: Support for incorporating teaching on artificial intelligence into core curricula was ubiquitous across the globe, but few students had received teaching on artificial intelligence. Students showed knowledge on the applications of artificial intelligence in clinical medicine as well as on artificial intelligence ethics. They were interested in learning about clinical applications, algorithm development, coding and algorithm appraisal. Hackathon-style projects and multidisciplinary education involving computer science students were suggested for incorporation into the curriculum. Conclusions: Medical students from all countries should be provided teaching on artificial intelligence as part of their curriculum to develop skills and knowledge around artificial intelligence to ensure a patient-centred digital future in medicine. This teaching should focus on the applications of artificial intelligence in clinical medicine. Students should also be given the opportunity to be involved in algorithm development. Students in low- and middle-income countries require the foundational technology as well as robust teaching on artificial intelligence to ensure that they can drive innovation in their healthcare settings.

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Figure 1. A bar plot of frequencies of students' answers to the question: ‘Which five of the following areas of medicine do you think could be most affected by the introduction of artificial intelligence?’. ‘Health systems management’ explicitly included hospital organisation and scheduling while the label ‘Administration’ included note-taking and secretaries.
Figure 1. A bar plot of frequencies of students' answers to the question: ‘Which five of the following areas of medicine do you think could be most affected by the introduction of artificial intelligence?’. ‘Health systems management’ explicitly included hospital organisation and scheduling while the label ‘Administration’ included note-taking and secretaries.CC BY · PubMed Central
Figure 2. A bar plot of frequencies of students' answers to the question: ‘How likely is it that you will be using AI in your clinical practice in the following time frames?’
Figure 2. A bar plot of frequencies of students' answers to the question: ‘How likely is it that you will be using AI in your clinical practice in the following time frames?’CC BY · PubMed Central

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Research works printed but not linked: 9

  1. Bi WL, 2019, CA: A Cancer J Clin, 69, 127
  2. Oakden-Rayner L, 2019, Radiol: Artif Intel, 1
  3. Wong BLH, 2021, Hum Resour Health, 19
  4. van Kessel R, 2022, Internet Intervention, 27
  5. Khurana MP, 2022, BMC Med Educ, 22
  6. Likert R, 1932, Arch of Psychol, 140, 5
  7. 10.2196/33819 · DOI
  8. Boutayeb A, 2010, In: Trans R Soc Trop Med Hyg, 100, 532
  9. Topol E. The topol review. Preparing the Healthcare Workforce to Deliver the Digital Future. Report on behalf of the Secretary of State for Health and Social Care, February 2019.

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Metadata

Authors
Ejaz, H., McGrath, H., Wong, B. L., Guise, A., Vercauteren, T., & Shapey, J.
Affiliations
  • University of East Anglia (GB)
  • King's College London (GB)
  • Department of International Health, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
  • School of Population Health and Environmental Sciences, King’s College London, UK
Corresponding author
  • Ejaz, Hamza
  • McGrath, Hari
ORCID
Venue
DIGITAL HEALTH
Publisher
SAGE Publications
Published
2022-01
Volume
8
Pages
205520762210890
ISSN
2055-2076 (ISSN-L)
DOI
10.1177/20552076221089099
PMC
PMC9067043
OpenAlex
W4229033576
Cited by
105
References
43
Citations by year
2026: 21 · 2025: 37 · 2024: 32 · 2023: 14
FWCI
6.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion, cc-by-nc-nd)
  • London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
  • London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion, cc-by)
  • Research Portal (King's College London) (submittedVersion, cc-by)
  • Lirias (KU Leuven) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Wellcome/EPSRC Centre for Medical Engineering ([WT203148/Z/16/Z])
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Innovations in Medical Education
  • Social Sciences › Education › Artificial Intelligence in Education
Keywords
artificial intelligence in medical education, medical students' perceptions, AI curriculum, AI ethics education, algorithm development, multidisciplinary education, hackathons, low- and middle-income countries

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