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Cited by 115Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationHealth and Medical Education

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

Artificial intelligence in medical education curriculum: An e-Delphi study for competencies

Open access
Çalışkan, S. Ayhan, Demir, Kadir, Karaca, Ozan·2022·PLOS ONE
DOI 10.1371/journal.pone.0271872PMC PMC9302857
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Abstract

BACKGROUND: Artificial intelligence (AI) has affected our day-to-day in a great extent. Healthcare industry is one of the mainstream fields among those and produced a noticeable change in treatment and education. Medical students must comprehend well why AI technologies mediate and frame their decisions on medical issues. Formalizing of instruction on AI concepts can facilitate learners to grasp AI outcomes in association with their sensory perceptions and thinking in the dynamic and ambiguous reality of daily medical practice. The purpose of this study is to provide consensus on the competencies required by medical graduates to be ready for artificial intelligence technologies and possible applications in medicine and reporting the results. MATERIALS AND METHODS: A three-round e-Delphi survey was conducted between February 2020 and November 2020. The Delphi panel accorporated experts from different backgrounds; (i) healthcare professionals/ academicians; (ii) computer and data science professionals/ academics; (iii) law and ethics professionals/ academics; and (iv) medical students. Round 1 in the Delphi survey began with exploratory open-ended questions. Responses received in the first round evaluated and refined to a 27-item questionnaire which then sent to the experts to be rated using a 7-point Likert type scale (1: Strongly Disagree-7: Strongly Agree). Similar to the second round, the participants repeated their assessments in the third round by using the second-round analysis. The agreement level and strength of the consensus was decided based on third phase results. Median scores was used to calculate the agreement level and the interquartile range (IQR) was used for determining the strength of the consensus. RESULTS: Among 128 invitees, a total of 94 agreed to become members of the expert panel. Of them 75 (79.8%) completed the Round 1 questionnaire, 69/75 (92.0%) completed the Round 2 and 60/69 (87.0%) responded to the Round 3. There was a strong agreement on the 23 items and weak agreement on the 4 items. CONCLUSIONS: This study has provided a consensus list of the competencies required by the medical graduates to be ready for AI implications that would bring new perspectives to medical education curricula. The unique feature of the current research is providing a guiding role in integrating AI into curriculum processes, syllabus content and training of medical students.

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Fig 1 Delphi study diagram.
Fig 1 Delphi study diagram.CC BY · PubMed Central
Fig 2 Example item presentation in Round 3 survey questionnaire.
Fig 2 Example item presentation in Round 3 survey questionnaire.CC BY · PubMed Central

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  1. O Karaca, 2020, From Artificial Intelligence Theory to Application in Education, 346

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Metadata

Authors
Çalışkan, S. A., Demir, K., & Karaca, O.
Affiliations
  • Ege University (TR)
  • İzmir Demokrasi Üniversitesi (TR)
Corresponding author
Çalışkan, S. Ayhan
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2022-07-21
Volume
17
Issue
7
Pages
e0271872
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0271872
PMC
PMC9302857
OpenAlex
W4286493308
Cited by
115
References
29
Citations by year
2026: 37 · 2025: 33 · 2024: 28 · 2023: 14 · 2022: 2
FWCI
5.80
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Innovations in Medical Education
  • Health Sciences › Health Professions › General Health Professions › Health and Medical Education
Keywords
artificial intelligence in healthcare, medical curriculum, AI competencies, medical graduates, competency framework, AI literacy, expert consensus, curriculum integration
MeSH terms
Artificial Intelligence, Curriculum, Delphi Technique, Education, Medical, Humans, Consensus

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