佟佳益, 郑改改, 王宇, 杨巧芳. 医学人工智能研究热点双聚类分析[J]. 实用临床医药杂志, 2024, 28(3): 13-17, 22. DOI: 10.7619/jcmp.20232042
引用本文: 佟佳益, 郑改改, 王宇, 杨巧芳. 医学人工智能研究热点双聚类分析[J]. 实用临床医药杂志, 2024, 28(3): 13-17, 22. DOI: 10.7619/jcmp.20232042
TONG Jiayi, ZHENG Gaigai, WANG Yu, YANG Qiaofang. Double clustering analysis of medical artificial intelligence research hotspots[J]. Journal of Clinical Medicine in Practice, 2024, 28(3): 13-17, 22. DOI: 10.7619/jcmp.20232042
Citation: TONG Jiayi, ZHENG Gaigai, WANG Yu, YANG Qiaofang. Double clustering analysis of medical artificial intelligence research hotspots[J]. Journal of Clinical Medicine in Practice, 2024, 28(3): 13-17, 22. DOI: 10.7619/jcmp.20232042

医学人工智能研究热点双聚类分析

Double clustering analysis of medical artificial intelligence research hotspots

  • 摘要:
    目的 采用双聚类方法对人工智能在医学领域的国际研究成果进行分析, 探讨主题领域内热点趋势。
    方法 检索Web of Science核心合集数据库中医学人工智能的相关文献,采用Co-Occurrence13.4提取高频关键词生成词篇矩阵,应用gCluto1.0聚类工具包进行双聚类分析。
    结果 共纳入文献7 803篇,年发文量整体呈上升趋势,美国位居发文总量的首位,共提取30个高频主题词,形成人工智能应用于生物标志物检测等6个聚类。研究热点聚焦于卫生保健、疾病转归、疾病全程监测、辅助诊断癌症、预测模型效验和鉴别生物标志物6个主题。
    结论 人工智能已普遍应用于临床诊断和治疗,为基因检测及公共卫生事件提供了针对性的支持,但国内相关研究还处于发展阶段,未来还需要依托多学科、机构间的交流合作,推动中国智能化医疗的发展,使其真正成为促进医疗卫生事业发展的重要工具。

     

    Abstract:
    Objective To analyze the international research results of artificial intelligence in the medical field by the double clustering method, and to explore the hot trends in the topic field.
    Methods The Web of Science core collection database was searched for the research literature of artificial intelligence in the field of medicine, and the high-frequency keywords were extracted by Co-Occurrence13.4 to generate the word matrix. The gCluto1.0 clustering toolkit was used for the double cluster analysis.
    Results A total of 7 803 articles were included, and the annual number of publications showed an overall upward trend. The United States ranked the first in the total number of publications. A total of 30 high-frequency subject words were extracted to form 6 clusters such as artificial intelligence applied to biomarker detection. The research hotspots focused on six topics: health care, disease outcome, whole-course disease monitoring, auxiliary diagnosis of cancer, model validity and differential biomarkers.
    Conclusion Artificial intelligence has been widely used in clinical diagnosis and treatment technology, which provides targeted support for genetic testing and public health events. However, related domestic research is still in developing stage. In the future, we need to rely on multidisciplinary and inter-institutional communication and cooperation to promote the development of intelligent medical in China, so that it truly becomes an important tool to promote the development of medical and health services.

     

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