Alsahafi Zaki, Baashar Ahmaed
Basic Sciences, College of Science and Health Professions, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU.
Research Office, King Abdullah International Medical Research Center, Jeddah, SAU.
Cureus. 2025 May 3;17(5):e83437. doi: 10.7759/cureus.83437. eCollection 2025 May.
This systematic review aims to analyze the existing literature on artificial intelligence (AI) applications in medical education in Saudi Arabia, and it spanned the period from January 2020 to February 2025. The review focuses on the nature and scope of AI applications, evidence synthesis types, geographical distribution of authorship, quality of research, challenges encountered, and research gaps within Saudi Arabia. Studies were retrieved from the PubMed, Google Scholar, ProQuest, and Web of Science databases. The process followed the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We included studies that explored knowledge, attitudes, and practices of AI among medical students and academics in Saudi Arabia. We first screened the titles and abstracts of the studies according to our inclusion criteria, and then reviewed the full texts of those that met the criteria. A standardized form was used to collect data, including author information, study population, research objectives, and key findings. The review identified key areas of focus, including personalized learning, interactive simulations, and real-time feedback in medical education. Most studies discussed the potential benefits of AI tools in improving student engagement and clinical decision-making skills. However, significant challenges were reported, such as insufficiencies in faculty training, data privacy concerns, and disparities in technological infrastructure. While the use of AI in medical education in Saudi Arabia has great potential, there are still significant challenges. There is a need for proper training for faculty and standardized AI curricula. More research is required to assess the long-term effects of AI on educational outcomes and find ways to overcome the current barriers to its successful implementation.
本系统评价旨在分析沙特阿拉伯人工智能（AI）在医学教育中应用的现有文献，时间跨度为2020年1月至2025年2月。该评价聚焦于AI应用的性质和范围、证据综合类型、作者的地理分布、研究质量、遇到的挑战以及沙特阿拉伯国内的研究空白。研究从PubMed、谷歌学术、ProQuest和科学网数据库中检索。该过程遵循系统评价和Meta分析的首选报告项目（PRISMA）所概述的指南。我们纳入了探索沙特阿拉伯医学生和学者对AI的知识、态度和实践的研究。我们首先根据纳入标准筛选研究的标题和摘要，然后对符合标准的研究进行全文审查。使用标准化表格收集数据，包括作者信息、研究人群、研究目标和主要发现。该评价确定了重点关注的关键领域，包括医学教育中的个性化学习、交互式模拟和实时反馈。大多数研究讨论了AI工具在提高学生参与度和临床决策技能方面的潜在好处。然而，也报告了重大挑战，如教师培训不足、数据隐私问题以及技术基础设施的差异。虽然AI在沙特阿拉伯医学教育中的应用潜力巨大，但仍存在重大挑战。需要对教师进行适当培训并制定标准化的AI课程。需要更多研究来评估AI对教育成果的长期影响，并找到克服当前其成功实施障碍的方法。
