Artificial Intelligence (AI) for Screening Mammography, from the AJR Special Series on AI Applications

Leslie R. Lamb, Constance D. Lehman, Aimilia Gastounioti, Emily F. Conant, Manisha Bahl

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)369-381
Number of pages13
JournalAmerican Journal of Roentgenology
Volume219
Issue number3
DOIs
StatePublished - Sep 2022

Keywords

  • artificial intelligence
  • breast cancer
  • implementation
  • machine learning
  • screening mammography

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