Performance of artificial intelligence in breast cancer screening programmes: a systematic review
Ghufran Jassim, Osamah Otoom, Bindhu Nair, Jamal Hashem · 2025
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Description
Ghufran Jassim, Osamah Otoom, Bindhu Nair, and Jamal Hashem review, in a 2025 paper, how well artificial intelligence performs inside breast cancer screening programs. Interest in AI readers for mammograms has grown fast, and the evidence base with it. Screening aims to catch cancer early in women without symptoms, where a missed case or a false alarm both carry real costs.
The team systematically searched eight sources, from 2012 to mid 2025, with no language limits. Eligible studies tested AI on screening mammography, either as a stand alone reader or as a help to radiologists, against radiologists alone. They extracted accuracy measures: sensitivity, specificity, recall rates, and cancer detection. Workflow outcomes, such as reading load, were also collected. The synthesis is meant to show screening programs what AI can and cannot yet do at population scale.
Catalog record
| CREATOR | Ghufran Jassim, Osamah Otoom, Bindhu Nair, Jamal Hashem |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |