Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114.
Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
AJR Am J Roentgenol. 2022 Sep;219(3):369-380. doi: 10.2214/AJR.21.27071. Epub 2022 Jan 12.
Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
人工智能（AI）应用于乳腺 X 线摄影筛查，目前正被推向临床应用领域，包括病变检测和诊断、分诊、乳腺密度评估等有意义的解释领域，以及乳腺癌风险评估、图像质量控制、图像获取和剂量降低等无意义的解释领域。支持这些新兴应用的证据，特别是在病变检测和诊断方面，主要基于多读者研究和富含癌症数据的数据集，而不是与应用的特定预期临床用途相匹配的严格临床评估。本文回顾了目前可用于临床实践的乳腺 X 线摄影筛查的商业 AI 算法、它们的用途以及支持其性能的证据。还描述了临床实施的注意事项，例如工作流程集成、治理和伦理问题。此外，还讨论了 AI 在乳腺 X 线摄影筛查中的未来，包括解释性和非解释性 AI 应用的发展以及研究和开发的战略重点。
