Department of Medical Imaging, University of Arizona, Tucson, AZ.
Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA.
Semin Ultrasound CT MR. 2023 Feb;44(1):2-7. doi: 10.1053/j.sult.2022.12.002. Epub 2022 Dec 26.
This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists' reading time. AI can also serve as a potential second 'reader' during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.
这篇专题综述聚焦于人工智能(AI)这一快速发展领域在临床乳房 X 线摄影成像中的应用。AI 的应用范围非常广泛。它可用于乳腺癌风险评估,从而可以针对每个女性量身定制筛查间隔和方案,并对筛查检查进行分类。它还可以作为一种工具,通过提高敏感性和特异性来辅助检测和诊断,并作为一种减少放射科医生阅读时间的工具。AI 还可以作为筛查解读时潜在的第二‘读者’。在过去的十年中,许多研究表明了 AI 辅助解读乳房 X 光摄影和在较小程度上数字乳腺断层合成的潜力;然而,这些研究大多是回顾性的。需要前瞻性的临床研究来评估这些技术,以更好地了解它们在现实世界中的效果。此外,在 AI 常规用于乳房成像诊所之前,还需要考虑伦理、医疗法律和责任方面的问题。