Deep Learning-Based Artificial Intelligence for Mammography

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.

Automated summary

AI-based algorithms in mammography interpretation have shown promising results in quantitative assessment, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. However, further research is needed to conclusively prove their effectiveness. Implementing AI algorithms also faces challenges in real-world practice.