Artificial intelligence for the real world of breast screening. Issue 144 (November 2021)

• Record Type: Journal Article
• Authors: Wallis, Matthew G.
• Is Part Of: European journal of radiology. Issue 144(2021)
• Publication Date: 2021-11
• Subjects: Breast cancer screening -- Artificial intelligence; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109661
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: In retrospective studies new AI algorithms approach performance of single readers. Design, origin, and definition of 'true positive' will all alter performance. Prospective studies need to demonstrate performance can be replicated in real life. To further reduce mortality AI algorithms must find more high-risk cancers earlier. Abstract: Breast cancer screening with mammography reduces mortality in the women who attend by detecting high risk cancer early. It is far from perfect with variations in both sensitivity for the detection of cancer and very wide variations in specificity, leading to unnecessary recalls and biopsies. Over the last 12 months several papers have reported on AI algorithms that perform as well as human readers on large well curated population data sets. The nature of the test sets, the way the gold standard has been calculated, the definition of a positive call, and the statistics used all influence the results. Historically retrospective studies have not predicted the real-life performance of radiologist plus machine. So, it is important to perform prospective studies before introducing Artificial intelligence into real world breast screening.