医学教育与人工智能:基于科学网的文献计量分析(2013-2022 年)》。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES JMIR Medical Education Pub Date : 2024-10-10 DOI:10.2196/51411
Shuang Wang, Liuying Yang, Min Li, Xinghe Zhang, Xiantao Tai
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引用次数: 0

摘要

背景:人工智能(AI)技术的逐步发展促进了其与各学科的融合。特别是,将人工智能注入医学教育已成为一种重要趋势,并取得了值得关注的研究成果。因此,有必要对当前人工智能在医学教育中的研究情况进行全面回顾和分析:本研究旨在利用 CiteSpace 和 VOSviewer 对 2013-2022 年间的相关论文进行文献计量分析。本研究直观地反映了人工智能在医学教育中的现有研究现状和趋势:在 Web of Science 核心数据库中系统检索了 2013 年至 2022 年间发表的与人工智能和医学教育相关的文章。两名审稿人根据论文标题和摘要对初步检索到的论文进行了仔细检查,以剔除与主题无关的论文。然后使用 CiteSpace 和 VOSviewer 对所选论文进行分析,并对国家、机构、作者、参考文献和关键词进行可视化处理:从 2013 年到 2022 年,共发现 195 篇与医学教育中的人工智能相关的论文。随着时间的推移,每年发表的论文呈上升趋势。美国是这一研究领域最活跃的国家,哈佛医学院和多伦多大学是最活跃的机构。该领域的著名作者包括文森特-比索内特、夏洛特-布莱克特、罗兰多-F-德尔-梅斯特罗、尼科尔-莱多斯、尼坎-米尔奇、亚历山大-温克勒-施瓦茨和雷凯-伊拉马兹。引用率最高的论文是 "医学生对人工智能的态度:多中心调查"。关键词分析显示,"放射学"、"医学物理学"、"电子健康"、"外科 "和 "专科 "是主要关注点,而 "大数据 "和 "管理 "则成为研究前沿:本研究强调了人工智能在医学教育研究中的巨大潜力。目前的研究方向包括放射学、医学信息管理和其他方面。技术进步有望进一步拓宽这些方向。加强地区间合作、提高研究质量迫在眉睫。这些研究结果为研究人员提供了宝贵的见解,有助于他们确定研究视角并指导未来的研究方向。
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Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).

Background: Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted.

Objective: This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education.

Methods: Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer.

Results: A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers.

Conclusions: The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

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来源期刊
JMIR Medical Education
JMIR Medical Education Social Sciences-Education
CiteScore
6.90
自引率
5.60%
发文量
54
审稿时长
8 weeks
期刊最新文献
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