Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study
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Published:2023-12-27
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ISSN:1432-1084
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Container-title:European Radiology
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language:en
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Short-container-title:Eur Radiol
Author:
Allam Ahmed HafezORCID, Eltewacy Nael Kamel, Alabdallat Yasmeen Jamal, Owais Tarek A., Salman Saif, Ebada Mahmoud A., Aldare Hajar Alkokhiya, Rais Mohammed Amir, Salem Moath, Al-Dabagh Jaafar D., Alhassan Monzer Abdulatif, Hanjul Marah M., Mugibel Tayba Abdulrahman, Motawea Sara Hamada, Hussein Mirna, Anas Omar Saeed, Amine Nacer Mohamed, Almekhlafi Moath Ahmed, Mugibel Muna Ali, Barhoom Eman Salem, Neiroukh Haroun, Shweiki Raghad, Balaw Mohammad Khalaf, Al-Slehat Mohmmad Ahmad, Roze Zaynab, Sadeq Maram A., Mokhtar Fathia, Babiker Noora Mahdi, Al-Ati Rami Abd, Alhoudairi Huda Adel, Attayeb Mohammed Omran, Abdulhadi Abdulrhman, Arja Abdulghani, Wardeh Abdulkareem Muhammad, Alakhrass Dana Nabil, Alkanj Souad,
Abstract
Abstract
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.
Funder
The Science, Technology & Innovation Funding Authority
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Reference28 articles.
1. Martín Noguerol T, Paulano-Godino F, Martín-Valdivia MT et al (2019) Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol 16:1239–1247. https://doi.org/10.1016/J.JACR.2019.05.047 2. Nichols JA, Herbert Chan HW, Baker MAB (2019) Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev 11:111–118. https://doi.org/10.1007/s12551-018-0449-9 3. Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31:685–695. https://doi.org/10.1007/s12525-021-00475-2 4. Lee JG, Jun S, Cho YW et al (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18:570–584. https://doi.org/10.3348/KJR.2017.18.4.570 5. Egger J, Gsaxner C, Pepe A et al (2022) Medical deep learning-a systematic meta-review. Comput Methods Programs Biomed 221:106874. https://doi.org/10.1016/j.cmpb.2022.106874
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