■近年来,在可用的工具中,人工智能(AI)的并行应用提高了乳腺癌筛查的诊断性能.在这种情况下,本研究旨在使用文献计量学分析对AI在乳腺癌诊断和预后研究中的进展进行全面概述.
■因此,在本研究中,从2000年至2021年发表的相关同行评审研究文章从Scopus和WebofScience(WOS)数据库下载,然后使用Bibliometrix(R包)进行定量分析和可视化。最后,确定了未来研究工作的开放挑战领域。
■本研究表明,从2000年到2021年,在AI上发表的用于乳腺癌检测和生存预测的文献研究数量从12增加到546。美利坚合众国(美国),中华民国,印度是这一领域最有成效的出版物。此外,美国在总引文方面领先;然而,饥饿和荷兰在每年的平均引文中处于领先地位。王杰是最有成效的作者,詹杰是这一领域最相关的作者。斯坦福大学在美国是最相关的从属关系发表的文章的数量。最相关的前10个来源是Q1期刊与PLOSONE和计算机在生物学和医学是该领域的领先期刊。与我们研究相关的最热门的话题,迁移学习和深度学习,已确定。
■目前的研究结果为决策者和学术研究人员提供了见解和研究方向,为乳腺癌患者未来的AI合作和研究提供了指导。
UNASSIGNED: In 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.
UNASSIGNED: Therefore, 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.
UNASSIGNED: The 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.
UNASSIGNED: The present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.