Use of artificial intelligence for image analysis in breast cancer screening programmes: Systematic review of test accuracy

Karoline Freeman, Julia Geppert, Chris Stinton, Daniel Todkill, Samantha Johnson, Aileen Clarke, Sian Taylor-Phillips*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women's digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590.

Original languageEnglish
Article numbern1872
JournalBritish Medical Journal
Publication statusPublished - 1 Sep 2021
Externally publishedYes

Bibliographical note

Funding Information:
Contributors: KF, JG, SJ, and CS undertook the review. SJ devised and managed the search strategy in discussion with the other authors. KF, JG, CS, DT, AC, ST-P contributed to the conception of the work and interpretation of the findings. KF drafted the manuscript. All authors critically revised the manuscript and approved the final version. ST-P takes responsibility for the integrity and accuracy of the data analysis. STP acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Funding: This study was funded by the UK National Screening Committee. The funder had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. Competing interests: All authors have completed the ICMJE uniform disclosure form at and declare: CS, ST-P, KF, JG, and AC have received funding from the UK National Screening Committee for the conduct of the review; ST-P is funded by the National Institute for Health Research (NIHR) through a career development fellowship; AC is partly supported by the NIHR Applied Research Collaboration West Midlands; SJ and DT have nothing to declare; no other relationships or activities that could appear to have influenced the submitted work.

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