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人工智能在牙科中的应用:基于文献计量学的综述,重点关注该领域内的计算研究趋势。

Artificial intelligence applications in dentistry: A bibliometric review with an emphasis on computational research trends within the field.

出版信息

J Am Dent Assoc. 2024 Sep;155(9):755-764.e5. doi: 10.1016/j.adaj.2024.05.013. Epub 2024 Aug 1.


DOI:10.1016/j.adaj.2024.05.013
PMID:39093229
Abstract

BACKGROUND: The aim of this study was to understand the trends regarding the use of artificial intelligence in dentistry through a bibliometric review. TYPES OF STUDIES REVIEWED: The authors performed a literature search on Web of Science. They collected the following data: articles-number and density of citations, year, key words, language, document type, study design, and theme (main objective, diagnostic method, and specialties); journals-impact factor; authors-country, continent, and institution. The authors used Visualization of Similarities Viewer software (Leiden University) to analyze the data and Spearman test for correlation analysis. RESULTS: After selection, 1,478 articles were included. The number of citations ranged from 0 through 327. The articles were published from 1984 through 2024. Most articles were characterized as proof of concept (979). Definition and classification of structures and diseases was the most common theme (550 articles). There was an emphasis on radiology (333 articles) and radiographic-based diagnostic methods (715 articles). China was the country with the most articles (251), and Asia was the continent with the most articles (871). The Charité-University of Medicine Berlin was the institution with the most articles (42), and the author with the most articles was Schwendicke (53). PRACTICAL IMPLICATIONS: Artificial intelligence is an important clinical tool to facilitate diagnosis and provide automation in various processes.

摘要

背景:本研究旨在通过文献计量学综述了解牙科人工智能应用的趋势。

研究类型综述:作者在 Web of Science 上进行了文献检索。他们收集了以下数据:文章数量和引文密度、年份、关键词、语言、文件类型、研究设计和主题(主要目标、诊断方法和专业);期刊影响因子;作者所在国家、大陆和机构。作者使用 Leiden University 的可视化相似性查看器软件对数据进行分析,并进行 Spearman 检验进行相关性分析。

结果:经过筛选,共纳入 1478 篇文章。引文数量从 0 到 327 不等。文章发表于 1984 年至 2024 年。大多数文章的特点是概念验证(979 篇)。结构和疾病的定义和分类是最常见的主题(550 篇)。放射学(333 篇)和基于放射学的诊断方法(715 篇)受到重视。中国发表的文章最多(251 篇),亚洲发表的文章最多(871 篇)。Charité-University of Medicine Berlin 是发表文章最多的机构(42 篇),发表文章最多的作者是 Schwendicke(53 篇)。

实际意义:人工智能是一种重要的临床工具,可促进诊断并为各种流程提供自动化。

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