Deep learning-based artificial intelligence for mammography

Jung Hyun Yoon, Eun Kyung Kim

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1225-1239
Number of pages15
JournalKorean journal of radiology
Issue number8
Publication statusPublished - 2021

Bibliographical note

Funding Information:
The authors thank Medical Illustration & Design, part of the Medical Research Support Services of Yonsei University College of Medicine, for artistic support related to this work.

Publisher Copyright:
© 2021 The Korean Society of Radiology.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging


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