Evidence map›Paper›PMID 38714993›Full record

ArticleBMC medical education2024

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine.

Kamel Jebreen, Eqbal Radwan, Wafa Kammoun-Rebai, Etimad Alattar, Afnan Radwan, Walaa Safi, Walaa Radwan, Mohammed Alajez

Abstract read
In one paragraph

Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kamel JebreenDepartment of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine.ORCID http://orcid.org/0000-0003-3933-0579
Eqbal RadwanDepartment of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine. ernp2030@gmail.com.ORCID http://orcid.org/0000-0001-5720-095X
Wafa Kammoun-RebaiMedical Research Direction, Ministry of Health, Tunis, Tunisia.ORCID http://orcid.org/0000-0002-2872-7282
Etimad AlattarDepartment of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.ORCID https://orcid.org/0009-0009-1629-7455
Afnan RadwanFaculty of Education, Islamic University of Gaza, Gaza, Palestine.ORCID https://orcid.org/0000-0002-2257-7818
Walaa SafiDepartment of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.ORCID https://orcid.org/0009-0008-2894-7892
Walaa RadwanUniversity College of Applied Sciences - Gaza, Gaza, Palestine.
Mohammed AlajezAl-Azhar University, Gaza, Palestine.ORCID https://orcid.org/0000-0003-3029-9018

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.

methodsThis study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.

resultsFrom a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

conclusionMedical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Indexed as

Artificial IntelligenceCurriculumEducation, Medical, UndergraduateStudents, MedicalAdultArabsAttitude of Health PersonnelCross-Sectional StudiesFemaleHumansMaleMiddle EastQualitative ResearchSurveys and QuestionnairesYoung AdultArtificial intelligenceCross-sectional studyMachine learningMedical educationMedical studentsPalestinePerceptions

Identifiers

PMID38714993
PMCPMC11077786

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.