Deep learning in breast radiology: current progress and future directions

William C. Ou, Dogan Polat, Basak E. Dogan

Research output: Contribution to journalReview articlepeer-review

16 Scopus citations


Abstract: This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification of breast lesions, but also into areas such as risk estimation and prediction of tumor responses to therapy. Remaining challenges include limited availability of high-quality data with expert annotations and ground truth determinations, the need for further validation of initial results, and unresolved medicolegal considerations. Key Points: • Deep learning (DL) continues to push the boundaries of what can be accomplished by artificial intelligence (AI) in breast imaging with distinct advantages over conventional computer-aided detection. • DL-based AI has the potential to augment the capabilities of breast radiologists by improving diagnostic accuracy, increasing efficiency, and supporting clinical decision-making through prediction of prognosis and therapeutic response. • Remaining challenges to DL implementation include a paucity of prospective data on DL utilization and yet unresolved medicolegal questions regarding increasing AI utilization.

Original languageEnglish (US)
Pages (from-to)4872-4885
Number of pages14
JournalEuropean Radiology
Issue number7
StatePublished - Jul 2021


  • Artificial intelligence
  • Breast magnetic resonance imaging
  • Breast ultrasonography
  • Deep learning
  • Digital mammography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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