Taylor, C.R.; Monga, N.; Johnson, C.; Hawley, J.R.; Patel, M. Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions. Diagnostics2023, 13, 2041.
Taylor, C.R.; Monga, N.; Johnson, C.; Hawley, J.R.; Patel, M. Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions. Diagnostics 2023, 13, 2041.
Taylor, C.R.; Monga, N.; Johnson, C.; Hawley, J.R.; Patel, M. Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions. Diagnostics2023, 13, 2041.
Taylor, C.R.; Monga, N.; Johnson, C.; Hawley, J.R.; Patel, M. Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions. Diagnostics 2023, 13, 2041.
Abstract
Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA approved AI applications for breast imaging, but adoption and utilization are widely variable and overall low. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer and many of the most exciting research and available AI applications focus on cancer detection for mammography. There are however multiple additional potential applications for AI in breast imaging including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.
Keywords
breast imaging; artificial intelligence; deep learning; machine learning; mammography; breast MRI; breast ultrasound; radiology workflow; computer-aided diagnosis; computer-aided detection
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
