Journal Article

Knowledge, attitude, and perception of Arabmedical students towards artificial intelligence in medicine and radiology: Amulti-national cross-sectional study

Wardeh, Abdulkareem Muhammad, Salem, Moath, Mokhtar, Fathia, Salman, Saif, Alakhrass, Dana Nabil, Aldare, Hajar Alkokhiya, Rais, Mohammed Amir, Al-Dabagh, Jaafar D, Abdulhadi, Abdulrhman, Alhassan, Monzer Abdulatif, Hanjul, Marah M, Hussein, Mirna, Anas, Omar Saeed, Motawea, Sara Hamada, Mugibel, Tayba Abdulrahman, Sadeq, Maram A, Owais, Tarek A, Balaw, Mohammad Khalaf, Alkanj, Souad, Mugibel, Muna Ali, Roze, Zaynab, Shweiki, Raghad, Attayeb, Mohammed Omran, Barhoom, Eman Salem, Babiker, Noora Mahdi, Neiroukh, Haroun, Ebada, Mahmoud A, Almekhlafi, Moath Ahmed, Alhoudairi, Huda Adel, Amine, Nacer Mohamed, Eltewacy, Nael Kamel, Alabdallat, Yasmeen Jamal, Al-Slehat, Mohmmad Ahmad, Arja, Abdulghani, Allam, Ahmed Hafez, Al-Ati, Rami Abd

2024

Overview

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 statementThis 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.

• Publisher: Springer Nature B.V
• Subject: Artificial intelligence / Attitudes / Careers / Cluster analysis / Cross-sectional studies / Curricula / Data collection / Deep learning / Education / Health care / Knowledge / Machine learning / Medical students / Medicine / Perception / Radiology / Regression analysis / Students / Training