Ozcan B Bersu, Patel Bhavika K, Banerjee Imon, Dogan Basak E
The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA.
Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
J Breast Imaging. 2023 May 22;5(3):248-257. doi: 10.1093/jbi/wbad007.
Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users.
乳腺成像中的人工智能（AI）是一个快速发展的领域，成果令人期待。尽管该领域近期发表了大量文献，但一些未解决的问题导致AI在乳腺放射科医生的日常临床实践中的应用有限。本文概述了AI在乳腺成像中的主要局限性，包括但不限于美国食品药品监督管理局（FDA）批准的算法数量有限，以及缺乏带有组织学金标准的注释数据集；对数据隐私、安全、算法透明度和偏差的担忧；以及伦理问题。最终，要想成功地将AI应用于临床护理，就需要采取深思熟虑的行动来应对这些挑战，乳腺成像社区及其他终端用户之间要做到透明，并共享AI实施工作流程、局限性和性能指标。
