目的:我们旨在评估医学生的知识,态度,以及对医学中人工智能(AI)的感知。
方法:跨国,多中心横断面研究于2022年3月至4月进行,对象为9个阿拉伯国家的本科医学生.这项研究利用了基于网络的问卷,在国家领导人和地方合作者的帮助下进行数据收集。进行Logistic回归分析以确定知识的预测因子,态度,以及参与者之间的感知。此外,聚类分析被用来确定他们的反应中的共享模式。
结果:在接受调查的4492名学生中,92.4%的人没有接受过正式的AI培训。关于AI和深度学习(DL),87.1%的知识水平较低。大多数学生(84.9%)认为人工智能将彻底改变医学和放射学。48.9%的人同意它可以减少对放射科医生的需求。具有高/中等AI知识和培训的学生同意认可AI取代放射科医生的可能性更高。减少他们的数量,与知识水平低/没有人工智能培训的人相比,不太可能将放射学视为职业。此外,大多数人同意人工智能将有助于疾病的自动检测和诊断。
结论:阿拉伯医学生在与AI有关的知识和培训方面表现出明显的不足。尽管如此,他们对人工智能在医学和放射学中的应用持积极态度,清楚地了解其对医疗保健系统和医学课程的重要性。
结论:这项研究强调了对阿拉伯医学生进行广泛的人工智能教育和培训的必要性,表明其对医疗保健系统和医学课程的重要性。
结论:•阿拉伯医学生在医学和放射学领域使用人工智能方面表现出明显的知识和培训差距。阿拉伯医学生认识到将人工智能融入医学课程的重要性。对人工智能有更深入了解的学生更有可能同意所有医学生都应该接受人工智能教育。然而,那些以前接受过人工智能培训的人对这个想法不太支持。•具有中/高AI知识和培训的学生显示,同意AI有潜力取代放射科医生的可能性增加。减少对他们服务的需求,不太愿意从事放射科的职业,与知识水平低/没有人工智能培训的学生相比。
OBJECTIVE: 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.
CONCLUSIONS: 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.
CONCLUSIONS: • 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.