Cited by 154Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index, and 1 are not checked yet.

Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education

Open access

Abstract

Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/healthcare11091298

Cites

42 of 43 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Previous teaching or training in artificial intelligence status of the study participants.
Figure 1 Previous teaching or training in artificial intelligence status of the study participants.CC BY · PubMed Central
Figure 2 Students’ perceptions of an understanding of artificial intelligence limitations, according to the academic year.
Figure 2 Students’ perceptions of an understanding of artificial intelligence limitations, according to the academic year.CC BY · PubMed Central
Figure 3 Students’ future expectation of better-understanding healthcare artificial intelligence performance, according to the academic year.
Figure 3 Students’ future expectation of better-understanding healthcare artificial intelligence performance, according to the academic year.CC BY · PubMed Central
Figure 4 Students’ expectation of possessing the knowledge to work with artificial intelligence in practice, according to the academic year.
Figure 4 Students’ expectation of possessing the knowledge to work with artificial intelligence in practice, according to the academic year.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 40 references

Research works printed but not linked: 2

  1. Elsevier (2022, June 25). Description—Artificial Intelligence in Medicine. Available online: https://www.elsevier.com/journals/artificial-intelligence-in-medicine/0933-3657/subscribe.
  2. Edward, H., Shortliffe, J.J.C., and Chiang, M.F. (2021). Biomedical Informatics: Computer Applications in Health Care and Biomedicine, Springer Nature. [5th ed.].

Cited by

Sorted by citations

Metadata

Authors
Buabbas, A. J., Miskin, B., Alnaqi, A. A., Ayed, A. K., Shehab, A. A., Syed-Abdul, S., & Uddin, M.
Affiliations
  • Kuwait University (KW)
  • Ministry of Health (KW)
  • Mubarak Al Kabeer Hospital (KW)
  • Taipei Medical University (TW)
  • King Saud bin Abdulaziz University for Health Sciences (SA)
Corresponding author
Buabbas, Ali Jasem
Venue
Healthcare
Publisher
MDPI AG
Published
2023-05-01
Volume
11
Issue
9
Pages
1298
ISSN
2227-9032 (ISSN-L)
OpenAlex
W4367625612
Cited by
152
References
40
Citations by year
2026: 36 · 2025: 62 · 2024: 49 · 2023: 5
FWCI
6.05
Type
journal article
Language
en
Open access
Locations
  • Healthcare (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
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, medical education, medical students, student perceptions, AI literacy, artificial intelligence in healthcare, future of medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review