Almansour Mohammad, Soliman Mona, Aldekhyyel Raniah, Binkheder Samar, Temsah Mohamad-Hani, Malki Khalid H
Department of Medical Education, College of Medicine, King Saud University, Riyadh, SAU.
Department of Pediatrics, College of Medicine, King Saud University, Riyadh, SAU.
Cureus. 2025 Mar 25;17(3):e81145. doi: 10.7759/cureus.81145. eCollection 2025 Mar.
Generative artificial intelligence (GAI) has introduced a new era of medical education by offering innovative solutions to critical challenges in teaching, assessment, and clinical training. This expanded review explores the current and potential applications of GAI across multiple domains, including personalized tutoring, enhanced academic administrative efficiency, and improved preparedness for daily learning interactions. Utilizing a narrative review methodology combined with expert analysis, this study involved a structured literature search in January 2025 across PubMed, Scopus, and Google Scholar, followed by iterative brainstorming sessions and expert evaluations to assess the feasibility and impact of various GAI applications. Six domain experts then appraised the feasibility and impact of GAI technologies across educational settings, resulting in 10 identified domains of application: Quality and Administration, Curriculum Development, Teaching and Learning, Assessment and Evaluation, Clinical Training, Academic Guidance, Student Research, Student Affairs, Internship Management, and Student Activities. Our findings highlight how GAI supports personalized learning - through adaptive tutoring and automated performance dashboards - while optimizing administrative tasks such as course registration and policy oversight. In addition, immersive simulations and virtual patient encounters reinforce clinical decision-making and practical skills. GAI-driven tools also streamline research processes via automated literature reviews and proposal refinement, ultimately fostering greater efficiency across academic environments. Despite these opportunities, ethical considerations remain a priority. Issues pertaining to data privacy, algorithmic bias, and equitable access must be addressed through robust regulatory frameworks and institution-wide policies. Overall, by embracing targeted, ethically guided implementations, GAI has the evolving potential to enhance educational quality, improve operational effectiveness, and equip future healthcare professionals with the adaptive skills needed in a patient-centered clinical landscape.
生成式人工智能（GAI）通过为教学、评估和临床培训中的关键挑战提供创新解决方案，开启了医学教育的新时代。这篇扩展综述探讨了GAI在多个领域的当前和潜在应用，包括个性化辅导、提高学术管理效率以及增强日常学习互动的准备。本研究采用叙述性综述方法并结合专家分析，于2025年1月在PubMed、Scopus和谷歌学术上进行了结构化文献检索，随后进行了反复的头脑风暴会议和专家评估，以评估各种GAI应用的可行性和影响。然后，六位领域专家评估了GAI技术在不同教育环境中的可行性和影响，确定了10个应用领域：质量与管理、课程开发、教学、评估与评价、临床培训、学术指导、学生研究、学生事务、实习管理和学生活动。我们的研究结果突出了GAI如何通过自适应辅导和自动化绩效仪表板支持个性化学习，同时优化课程注册和政策监督等管理任务。此外，沉浸式模拟和虚拟患者体验强化了临床决策和实践技能。GAI驱动的工具还通过自动文献综述和提案完善简化了研究过程，最终提高了整个学术环境的效率。尽管有这些机会，但伦理考量仍然是首要任务。必须通过强有力的监管框架和全机构政策来解决与数据隐私、算法偏见和公平获取相关的问题。总体而言，通过采用有针对性的、符合伦理指导的实施方式，GAI有不断发展的潜力来提高教育质量、改善运营效果，并使未来的医疗保健专业人员具备以患者为中心的临床环境所需的适应能力。
