乳腺摄影术
工作流程
医学
人工智能
口译(哲学)
工作量
机器学习
乳腺癌
乳腺癌筛查
人工智能应用
深度学习
医学物理学
计算机科学
癌症
操作系统
内科学
数据库
程序设计语言
作者
Jung Hyun Yoon,Min Jung Kim
出处
期刊:Korean Journal of Radiology
[The Korean Society of Radiology]
日期:2021-01-01
卷期号:22 (8): 1225-1225
被引量:35
标识
DOI:10.3348/kjr.2020.1210
摘要
During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.
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