医学
乳腺癌
工作流程
医疗保健
工作量
软件部署
乳房成像
乳腺癌筛查
转化式学习
医学物理学
技术
稀缺
人工智能应用
人工智能
乳腺摄影术
计算机科学
癌症
心理学
教育学
数据库
内科学
经济
微观经济学
经济增长
操作系统
作者
Vivianne Freitas,Sandeep Ghai,Frederick Au,Derek Muradali,Supriya Kulkarni
标识
DOI:10.1177/08465371241301957
摘要
The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. This combination aims to address several challenges inherent in traditional screening while promising an improvement in healthcare delivery across multiple dimensions. For patients, this technological synergy has the potential to lower the number of unnecessary recalls and associated procedures such as biopsies, thereby reducing patient anxiety and improving overall experience without compromising diagnostic accuracy. For radiologists, the use of combined AI and DBT could significantly decrease workload and reduce fatigue by effectively highlighting breast imaging abnormalities, which is especially beneficial in high-volume clinical settings. Health systems stand to gain from streamlined workflows and the facilitated deployment of DBT, which is particularly valuable in areas with a scarcity of specialized breast radiologists. However, despite these potential benefits, substantial challenges remain. Bridging the gap between the development of complex AI algorithms and implementation into clinical practice requires ongoing research and development. This is essential to optimize the reliability of these systems and ensure they are accessible to healthcare providers and patients, who are the ultimate beneficiaries of this technological advancement. This article reviews the benefits of combined AI-DBT imaging, particularly the ability of AI to enhance the benefits of DBT and reduce its existing limitations.
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