Evidence map›Paper›PMID 38150076›Full record

ArticleEuropean radiology2024

Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study.

Ahmed Hafez Allam, Nael Kamel Eltewacy, Yasmeen Jamal Alabdallat, Tarek A Owais, Saif Salman, Mahmoud A Ebada, EARG Group

Abstract readMulticenter Study
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 3 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

46 citing papers in PubMed, 3 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

7 authors.

Ahmed Hafez AllamFaculty of Medicine, Menoufia University, Shebin El-Kom, Menoufia, Egypt. ahmedhafez21@med.menofia.edu.eg.ORCID http://orcid.org/0000-0002-8696-1165
Nael Kamel EltewacyEltewacy Arab Research Group, Cairo, Egypt.
Yasmeen Jamal AlabdallatEltewacy Arab Research Group, Cairo, Egypt.
Tarek A OwaisEltewacy Arab Research Group, Cairo, Egypt.
Saif SalmanEltewacy Arab Research Group, Cairo, Egypt.
Mahmoud A EbadaEltewacy Arab Research Group, Cairo, Egypt.
EARG Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.

methodsA multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses.

resultsOf the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies.

conclusionsArab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum. CLINICAL RELEVANCE STATEMENT: This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula. KEY POINTS: • Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology. • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea. • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeRadiologyStudents, MedicalAdultArabsAttitude of Health PersonnelCross-Sectional StudiesEducation, Medical, UndergraduateFemaleHumansMaleMiddle EastSurveys and QuestionnairesYoung AdultAI educationAI perceptionArtificial intelligenceMedicalRadiology trainingStudents

Identifiers

PMID38150076
PMCPMC11213794

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.