TY - JOUR
T1 - Artificial Intelligence (AI) for Screening Mammography, from the AJR Special Series on AI Applications
AU - Lamb, Leslie R.
AU - Lehman, Constance D.
AU - Gastounioti, Aimilia
AU - Conant, Emily F.
AU - Bahl, Manisha
N1 - Funding Information:
Supported by The Susan G. Komen Foundation (grant PDF17479714 to A. Gastounioti) and NIH (grant K08CA241365 to M. Bahl). We thank Susanne L. Loomis (Medical and Scientific Communications, Strategic Communications, Department of Radiology, Massachusetts General Hospital, Boston, MA) for creating Figures 1 and 4–7 in this article.
Funding Information:
Submitted: Nov 1, 2021 Revision requested: Nov 15, 2021 Revision received: Dec 14, 2021 Accepted: Jan 3, 2022 First published online: Jan 12, 2022 C. D. Lehman receives institutional research support from the Breast Cancer Research Foundation, GE Healthcare, and Hologic and is a cofounder of Clairity. A. Gastounioti receives research support from iCAD. E. F. Conant receives research support from Hologic, iCAD, and OM1; is a member of advisory panels of Hologic and iCAD; and has received speaker fees from AuntMinnie. com. M. Bahl is a consultant for Lunit and an expert panelist for 2nd.MD. The remaining author declares that there are no additional disclosures relevant to the subject matter of this article.
Funding Information:
Supported by the Susan G. Komen Foundation (grant PDF17479714 to A. Gastounioti) and NIH (grant K08CA241365 to M. Bahl).
Publisher Copyright:
© American Roentgen Ray Society.
PY - 2022/9
Y1 - 2022/9
N2 - Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application’s specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
AB - Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application’s specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
KW - artificial intelligence
KW - breast cancer
KW - implementation
KW - machine learning
KW - screening mammography
UR - http://www.scopus.com/inward/record.url?scp=85125344928&partnerID=8YFLogxK
U2 - 10.2214/AJR.21.27071
DO - 10.2214/AJR.21.27071
M3 - Review article
C2 - 35018795
AN - SCOPUS:85125344928
SN - 0361-803X
VL - 219
SP - 369
EP - 381
JO - American Journal of Roentgenology
JF - American Journal of Roentgenology
IS - 3
ER -