Table_5_Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis.docx

ObjectiveIn recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis.
MethodologyTherefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work.
ResultsThe present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified.
ConclusionThe present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.

研究目的
近年来，在各类可用工具中，人工智能（Artificial Intelligence）的协同应用已显著提升了乳腺癌筛查的诊断效能。在此背景下，本研究拟采用文献计量分析方法，全面梳理人工智能在乳腺癌诊断与预后研究领域的发展脉络。
研究方法
为此，本研究从Scopus与Web of Science（WOS）数据库中下载了2000年至2021年发表的相关同行评议研究论文，随后借助Bibliometrix（R语言包）开展定量分析与可视化研究，并最终明确了未来研究亟待攻克的开放挑战领域。
研究结果
本研究显示，2000年至2021年间，聚焦人工智能用于乳腺癌检测与生存预测的文献数量从12篇增长至546篇。在发文量维度，美国、中国与印度是该领域产出最多的国家；就总被引量而言，美国稳居首位，而匈牙利与荷兰则在篇均年被引量上占据领先地位。本领域发文量最高的作者为Wang J，最具学术相关性的作者为Zhan J；按发文量统计，美国斯坦福大学是该领域最具影响力的科研机构。本领域排名前十的核心刊载来源均为Q1分区期刊，其中《PLOS ONE》与《Computer in Biology and Medicine》为该领域的领军期刊。本研究还识别出当前研究热点方向，即迁移学习（Transfer Learning）与深度学习（Deep Learning）。
研究结论
本研究结果可为政策制定者与学术研究者开展乳腺癌人工智能相关的未来合作与研究提供思路与方向。