ArticleEuropean radiology2024
Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study.
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.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.JMIR medical education · 2026Pooled it
- Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students' perspectives: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Assessing the effectiveness of artificial intelligence education and training for healthcare workers: a systematic review.BMC medical education · 2026Pooled it
- Knowledge, Attitudes, and Practices Related to AI in Learning and Research Among Medical Students in Vietnam: Cross-Sectional Study.JMIR formative research · 2026Article
- Patient attitudes toward artificial intelligence in Jordanian Healthcare: A cross-sectional survey study.PLOS digital health · 2026Article
- Adoption of artificial intelligence tools among pharmacy students in Syria: patterns of use, educational perceptions, and institutional barriers.BMC medical education · 2026Article
- Knowledge, Attitude, and Perception of Medical Students Toward AI-Based Learning: A Cross-Sectional Study.Public health challenges · 2026Article
- Artificial intelligence in nursing education: knowledge, perceptions, and associated factors among Egyptian nursing students across universities.BMC nursing · 2026Article
- Knowledge, attitude, and perception of artificial intelligence among medical residents in Oman: readiness for clinical practice.BMC medical education · 2026Article
- Knowledge, perception, and attitude of healthcare students towards artificial intelligence: a multi-center cross sectional study.BMC medical education · 2026Article
- AI knowledge, attitudes, perceptions, and willingness to use AI among oncologists in China: a nationwide cross-sectional study.BMC medical education · 2026Article
- Education Research: Bridging the Artificial Intelligence Training Gap: Evidence from a National Survey of Italian Neurology Residents.Neurology. Education · 2026Article
- Dental students' knowledge, perceptions, and educational needs regarding artificial intelligence: a multinational cross-sectional survey.BMC medical education · 2026Article
- A study on promoting AI learning and usage behaviors among health management students from the perspective of the "knowledge-belief-action" model.Frontiers in public health · 2026Article
- Medical Students' Perceptions and Attitude Toward the Use of Artificial Intelligence in Healthcare.Advances in medical education and practice · 2026Article
- Knowledge, attitudes, and practices of pharmacy students and graduates regarding artificial intelligence in healthcare: a multi-regional cross-sectional study predominantly from the Asir region of Saudi Arabia.Frontiers in public health · 2026Article
- Article
- Application of Artificial Intelligence in Medical Education: A Systematic and Narrative Review of Pedagogical Potential and Ethical Implications.Advances in medical education and practice · 2026Review
- Perceived knowledge, attitude, and practice of artificial intelligence among medical students in Guangxi: a cross-sectional study.Frontiers in public health · 2026Article
- Knowledge, Attitude, and Perception of Artificial Intelligence in Healthcare Among Postgraduate Residents: A Cross-sectional Survey.Oman medical journal · 2026Article
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7 authors.
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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.
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