医学教育和人工智能:基于科学的图书识别分析网络 (2013-2022)
Shuang Wang1, Liuying Yang1, Min Li1
1Second Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, China.
JMIR medical education
|October 10, 2024
概括
这种图书统计分析揭示了医疗教育中人工智能 (AI) 研究的日益增长的趋势. 关键领域包括放射学和电子健康,有机会加强协作和提高AI应用的质量.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 图书统计学 图书统计学
背景情况:
- 人工智能 (AI) 越来越多地融入各种学科,特别是医学教育.
- 在医学教育研究中对人工智能的全面审查是有必要的,以了解当前的趋势和发现.
研究的目的:
- 在2013-2022年期间,对人工智能在医学教育研究中的应用进行文献分析.
- 使用CiteSpace和VOSviewer可视地表示研究现状和趋势.
主要方法:
- 在科学网络核心数据库中系统搜索人工智能和医学教育文章 (2013-2022年).
- 通过标题和摘要对论文进行选,然后使用CiteSpace和VOSviewer进行分析.
- 国家,机构,作者,参考文献和关键词的可视化.
主要成果:
- 在2013-2022年期间发表的195篇论文显示了日益增长的趋势.
- 美国领导研究活动;哈佛医学院和多伦多大学是领先的机构.
- 关键词表明重点是放射学,电子健康,外科手术",大数据"和"管理"作为新兴的前沿.
结论:
- 人工智能在医学教育研究中显示出巨大的潜力,目前专注于放射学和信息管理.
- 未来的研究应该扩大这些方向,强调区域间合作和提高研究质量.
- 结果为研究人员提供了洞察力,以指导医疗教育人工智能的未来方向.
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