Li Rui, Wu Tong
Emergency Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Interact J Med Res. 2025 Jan 30;14:e63775. doi: 10.2196/63775.
BACKGROUND: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain. OBJECTIVE: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century. METHODS: Documents were retrieved from the Web of Science Core Collection database from 2000 to 2024. VOSviewer, Incites, and Citespace were used to analyze the bibliometric metrics, which were categorized by country, institution, authors, journals, and keywords. The variables analyzed encompassed counts, citations, H-index, impact factor, and collaboration metrics. RESULTS: Altogether, 7534 publications were initially retrieved and 2775 were included for analysis. The annual count and citation of papers exhibited exponential trends since 2018. The United States emerged as the lead contributor due to its high productivity and recognition levels. Stanford University, Johns Hopkins University, National University of Singapore, Mayo Clinic, University of Arizona, and University of Toronto were representative institutions in their respective fields. Cureus, JMIR Medical Education, Medical Teacher, and BMC Medical Education ranked as the top four most productive journals. The resulting heat map highlighted several high-frequency keywords, including performance, education, AI, and model. The citation burst time of terms revealed that AI technologies shifted from imaging processing (2000), augmented reality (2013), and virtual reality (2016) to decision-making (2020) and model (2021). Keywords such as mortality and robotic surgery persisted into 2023, suggesting the ongoing recognition and interest in these areas. CONCLUSIONS: This study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits.
背景:将人工智能(AI)融入医学教育因其提升教学效果的潜力而备受关注。然而,目前缺乏对医学教育领域中人工智能的学术表现和现状的全面研究。 目的:本研究旨在分析21世纪以来人工智能在医学教育领域的社会模式、高产贡献者、知识结构和研究集群。 方法:从2000年至2024年的Web of Science核心合集数据库中检索文献。使用VOSviewer、InCites和CiteSpace分析文献计量指标,这些指标按国家、机构、作者、期刊和关键词进行分类。分析的变量包括论文数量、引用次数、H指数、影响因子和合作指标。 结果:最初共检索到7534篇出版物,其中2775篇纳入分析。自2018年以来,论文的年发表量和引用量呈指数趋势。美国因其高生产力和认可度成为主要贡献国。斯坦福大学、约翰·霍普金斯大学、新加坡国立大学、梅奥诊所、亚利桑那大学和多伦多大学是各自领域的代表性机构。Cureus、JMIR Medical Education、Medical Teacher和BMC Medical Education是发表量最高的四大期刊。生成的热点图突出了几个高频关键词,包括性能、教育、人工智能和模型。术语的引用爆发时间表明,人工智能技术从图像处理(2000年)、增强现实(2013年)和虚拟现实(2016年)转向决策(2020年)和模型(2021年)。死亡率和机器人手术等关键词一直持续到2023年,表明这些领域持续受到关注和研究兴趣。 结论:本研究为对教育技术感兴趣的研究人员提供了有价值的见解和指导,同时为前沿机构和期刊投稿提供了建议。随着人工智能的快速发展,医学教育有望获得更多益处。
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