Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study
Open accessAbstract
Objectives: We aimed to assess undergraduate medical students’ knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine. Methods: A 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. Results: Of 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. Conclusions: Arab 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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4,267 out of 4,492 participants (95%) showed interest in learning AI fundamentals.
“4,267 out of 4,492 participants (95%) showing interest in learning AI fundamentals [24]”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [24]
The Results (Attitude) and Discussion report that most of the 4,492 surveyed students supported AI education. Our copy does not report that 4,267 students (95%) were interested in learning AI fundamentals; a search for 4267 found no matching result. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThere is a disparity in AI exposure between students in HICs and LMICs, where formal AI education remains limited or absent.
“The disparity in AI exposure between students in HICs and LMICs—where formal AI education remains limited or absent [24, 41]—underscores persistent global inequities in access to AI-related educational resources”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [24]
The Results, under “Artificial intelligence training” (page not specified), report that 92.4% of surveyed Arab medical students received no AI teaching or training, supporting limited access to formal AI education in this study population. The paper does not establish a disparity in AI exposure between students in high-income and low- or middle-income countries. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA multinational study revealed that over 92% of medical students had not received formal AI education, and approximately 87% only had a basic understanding of AI.
“A multinational study revealed that over 92% of medical students had not received formal AI education, and approximately 87% only had a basic understanding of AI [34].”
From Integrating AI in medical education: a comprehensive study of medical students’ attitudes, concerns, and behavioral intentions (2025) · cites this paper as [34]
Despite enthusiasm about integrating AI in HPE and increased research, there is a lack of basic literacy and understanding of AI technology among HPE students.
“Despite enthusiasm about integrating AI in HPE and the increased amount of research on this topic, there is a lack of basic literacy and understanding of AI technology among HPE students [6, 17–20].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [6]
A cross-sectional comparative study of Arab medical students (N = 4492) from nine countries showed that 92.4% of students had not received formal AI knowledge or training.
“a cross-sectional comparative study of Arab medical students (N = 4492) from nine countries (Libya, Egypt, Iraq, Jordan, Syria, Sudan, Algeria, Palestine, and Yemen) showed that 92.4% of students had not received formal AI knowledge or training”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [6]
The study's finding of low AI literacy among HPE students is consistent with previous studies.
“Our results were consistent with previous studies that demonstrated a similar low literacy level in AI among HPE students.”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [6]
The same cross-sectional comparative study of Arab medical students showed that 87.1% had low knowledge in basic AI concepts, including machine learning and deep learning.
“and 87.1% had low knowledge in basic AI concepts, including machine learning and deep learning [6].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [6]
Participants in all of the above studies expressed enthusiasm and positive attitudes toward integrating AI in educational curricula and readiness to learn about AI.
“Interestingly, participants in all of the above studies expressed enthusiasm and positive attitudes toward integrating AI in educational curricula and readiness to learn about AI.”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [6]
Lack of expertise, lack of proper training, and time constraints were the main barriers to integrating AI in HPE in similar studies from Arab countries.
“Lack of expertise, lack of proper training, and time constraints were the main barriers to integrating AI in HPE in similar studies from Arab countries [6, 20].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [6]
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References 26 · Cited by 77
Research works printed but not linked: 1
- [24]European Society of Radiology (ESR) What the radiologist should know about artificial intelligence - an ESR white paper Insights Imaging 2019 10 44 10.1186/s13244-019-0738-2 30949865 PMC6449411 · DOI
- Authors
- Allam, A. H., Eltewacy, N. K., Alabdallat, Y. J., Owais, T. A., Salman, S., Ebada, M. A., for the EARG Group, Aldare, H. A., Rais, M. A., Salem, M., Al-Dabagh, J. D., Alhassan, M. A., Hanjul, M. M., Mugibel, T. A., Motawea, S. H., Hussein, M., Anas, O. S., Amine, N. M., Almekhlafi, M. A., Mugibel, M. A., Barhoom, E. S., Neiroukh, H., Shweiki, R., Balaw, M. K., Al-Slehat, M. A., Roze, Z., Sadeq, M. A., Mokhtar, F., Babiker, N. M., Al-Ati, R. A., Alhoudairi, H. A., Attayeb, M. O., Abdulhadi, A., Arja, A., Wardeh, A. M., Alakhrass, D. N., & Alkanj, S.
- Affiliations
- Menoufia University (EG)
- Beni-Suef University (EG)
- Hashemite University (JO)
- Mayo Clinic in Florida (US)
- Zagazig University (EG)
- Tanta University (EG)
- Alexandria University (EG)
- Ain Shams University (EG)
- University of Blida (DZ)
- Hadhramout University (YE)
- An-Najah National University (PS)
- University of Jordan (JO)
- University of Baghdad (IQ)
- University of Tripoli (LY)
- University of Aleppo (SY)
- Latakia University (SY)
- Homs University (SY)
- Corresponding author
- Allam, Ahmed Hafez
- ORCID
- Venue
- European Radiology
- Published
- 2023-12-27
- Volume
- 34
- Issue
- 7
- Pages
- 1-14
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC11213794
- OpenAlex
- W4390271900
- Cited by
- 77
- References
- 26
- Citations by year
- 2026: 31 · 2025: 25 · 2024: 20 · 2020: 1
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Radiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Greater South Information System
- Funders
- The Science, Technology & Innovation Funding Authority
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence in healthcare, artificial intelligence in radiology, medical students, knowledge, attitudes and perceptions, AI training, deep learning, radiology workforce, medical curriculum, Arab world
- MeSH terms
- Adult, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Education, Medical, Undergraduate, Female, Humans, Health Knowledge, Attitudes, Practice, Male, Middle East, Surveys and Questionnaires, Radiology, Students, Medical, Arabs, Young Adult
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