Mapping Key Nodes and Global Trends in AI and Large Language Models for Medical Education: A Bibliometric Study.

IF 1.7 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Advances in Medical Education and Practice Pub Date : 2025-08-14 eCollection Date: 2025-01-01 DOI:10.2147/AMEP.S538362
Kaining Lu, Shuben Sun, Wanzhang Liu, Junhui Jiang, Zejun Yan
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引用次数: 0

Abstract

Background: Artificial intelligence (AI) and large language models (LLMs) are transforming medical education by enhancing teaching and assessment methods. Research output has surged, but key bibliometric trends remain underexplored.

Methods: We retrieved 547 publications using the Web of Science Core Collection and conducted bibliometric analysis with CiteSpace and other bibliometric tools to examine publication volume, collaboration networks, citations, keywords, and other important bibliometric indicators.

Results: The United States, the United Kingdom and China lead publication output, with institutions like the University of London, the National University of Singapore and Harvard University at the forefront. JMIR Medical Education is a pivotal journal. Research on ChatGPT and LLMs dominates, with growing focus on nursing education, digital health, medical exams, and medical ethics. Clinical reasoning, undergraduate education, and virtual reality have been identified as underexplored areas of research.

Conclusion: AI and LLMs in medical education constitute a fast-evolving field, with journal calls shaping its bibliometric landscape and advancing the discipline. The field remains in a developmental phase, with subfields yet to be clearly defined. Topics such as nursing education, digital health, medical examinations, and conversational agents are gaining traction. Research on ChatGPT and LLMs holds a central and influential role. Emerging areas of focus include medical ethics, training methodologies, and skills development. Clinical reasoning, undergraduate education, and virtual reality in AI and LLMs for medical education are understudied, offering research opportunities.

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绘制关键节点和全球趋势的人工智能和医学教育大语言模型:文献计量学研究。
背景:人工智能(AI)和大语言模型(llm)正在通过改进教学和评估方法来改变医学教育。研究产出激增,但关键的文献计量趋势仍未得到充分探索。方法:利用Web of Science核心馆藏检索547篇文献,利用CiteSpace等文献计量工具对文献数量、合作网络、被引频次、关键词等重要文献计量指标进行计量分析。结果:美国、英国和中国在论文发表量方面处于领先地位,其中伦敦大学、新加坡国立大学和哈佛大学等机构位居前列。JMIR医学教育是一本举足轻重的期刊。ChatGPT和llm的研究占主导地位,越来越多的关注护理教育、数字健康、医学考试和医学伦理。临床推理、本科教育和虚拟现实已被确定为尚未开发的研究领域。结论:医学教育中的人工智能和法学硕士构成了一个快速发展的领域,期刊电话会议塑造了其文献计量学景观,并推动了该学科的发展。该领域仍处于开发阶段,子领域尚未明确定义。诸如护理教育、数字健康、医疗检查和会话代理等主题正在获得关注。ChatGPT和llm的研究具有核心和重要的作用。新兴的重点领域包括医学伦理、培训方法和技能发展。临床推理、本科教育、人工智能中的虚拟现实和医学教育的法学硕士都得到了充分的研究,提供了研究机会。
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来源期刊
Advances in Medical Education and Practice
Advances in Medical Education and Practice EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
3.10
自引率
10.00%
发文量
189
审稿时长
16 weeks
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