| 唐雅伦,高磊,高堂珂,等.面部识别技术在数智医学领域应用研究的可视化分析[J].安徽医药,2026,30(1):112-119. |
| 面部识别技术在数智医学领域应用研究的可视化分析 |
| Application of facial recognition technology in intelligent medicine: a visual analysis |
| |
| DOI：10.3969/j.issn.1009-6469.2026.01.023 |
| 中文关键词: 面部识别 人工智能 数智医学 文献计量 可视化分析 |
| 英文关键词: Facial recognition Artificial intelligence Intelligent medicine Bibliometrics Visual analytics |
| 基金项目:国家自然科学基金面上项目( 8217152484)；北京市科学技术委员会课题项目( Z221100003522029) |
|
| 摘要点击次数: 397 |
| 全文下载次数: 166 |
| 中文摘要: |
| 目的探究面部识别技术在数智医学领域的发展轨迹、研究热点和发展趋势，旨在为数智医学研究提供潜在研究热点。方法检索 Web of Science数据库中 1990—2024年关于面部识别技术在医学领域研究的文章，筛选后使用 CiteSpace、VOSviewer、R语言进行可视化分析。结果该研究共纳入 2004—2024年发表的 379篇文章，发文量自 2018年后呈指数级增长。各个国家发文量差异较大，马太效应显著，多数论文为中国、美国、印度等少数国家所著。美国在该领域前沿发展、学术影响力方面，处于世界领先地位。科研机构的研究以地域性合作为主。研究热点集中在疾病诊断、病情监测、心理健康评估、辅助诊疗等方面。面部表型特征识别是未来研究趋势。结论面部识别已成为医学与人工智能交叉学科领域研究的热点，助力传统医疗向数智化精准医疗转化，具有巨大的应用前景。 |
| 英文摘要: |
| Objective To explore the research history, hotspots, and development trends of facial recognition technology in the fieldof intelligent medicine, with the aim of identifying potential research directions for studies in intelligent medicine.Methods Articles published between 1990 and 2024 related to facial recognition technology in medicine were retrieved from the Web of Science database.Bibliometric analyses were performed using CiteSpace, R-based software Bibliometrix and VOSviewer.Results A total of 379 articles published between 2004 and 2024 were included in this study, showing exponential growth in publication volume since 2018. A nota-ble disparity existed in the volume of articles published by different countries, with a predominant "Matthew effect," where most publi-cations originated from a few countries, namely China, the United States, and India. The United States leaded in pioneering develop-ments and academic influence within this field. Research was predominantly influenced by geographical collaborations among scientif-ic institutions. Current research focused on disease diagnosis, condition monitoring, mental health assessment, and assisted diagnosisand treatment, with facial phenotypic feature recognition emerging as a prominent trend for future exploration.Conclusion Facial rec-ognition has emerged as a hotspot in the interdisciplinary field of medicine and artificial intelligence, facilitating the transformation oftraditional healthcare into intelligent precision medicine and presenting significant application prospects. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
