Abstract：
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a \"one-stop center\" synthesis and provide a holistic bird\'s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
摘要：
人工智能(AI)一个令人振奋的进步，扰乱了广泛的应用，在过去的几十年里，势头继续强劲。在乳房成像中，AI,尤其是机器学习和深度学习，用无限的交叉数据/案例引用磨练，发现了包含四个方面的巨大效用：筛查和检测，诊断,疾病监测,和整个数据管理。多年来,乳腺癌一直是六大洲女性癌症累积风险排名的最高点，以各种形式存在，并在医疗决策中提供了复杂的背景。实现对高质量医疗保健的不断增长的需求，当代人工智能已经被设想在临床数据管理和感知方面取得长足进步，有能力检测不确定的重要性，预测预测，并将可用数据关联到有意义的临床终点。这里,作者捕捉到了过去几十年的评论作品，专注于乳腺成像中的人工智能，并将所包含的作品系统化为一份可用文件，这被称为伞式审查。本研究旨在提供AI如何准备增强乳房成像程序的全景视图。根据系统评价和荟萃分析（PRISMA）指南的首选报告项目进行循证科学计量分析。导致71项纳入审查工作。本研究旨在综合，整理,并关联所包含的审查工作，从而识别模式，趋势,质量,以及所包含作品的类型，由结构化搜索策略捕获。本研究旨在作为“一站式中心”的综合，并为读者提供一个整体的鸟瞰，从新来者到现有研究人员和相关利益相关者，关于感兴趣的话题。
