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This narrative review explores current and emerging AI applications in mammography screening, including image-based cancer detection, risk prediction, and workflow optimization, with attention to technical foundations, performance metrics, and clinical utility. Evidence indicates that AI may enhance diagnostic accuracy, enable more personalized risk assessment and screening strategies, and reduce radiologist workload, which has implications for accessibility, especially in resource-limited settings with radiologist shortages. However, real-world implementation of these tools remains challenging due to limitations in algorithm generalizability to diverse populations, calibration and reader response behaviour concerns, as well as regulatory, ethical and legal obstacles. While the potential impact is considerable, broader adoption will depend on prospective validation, transparent performance reporting, and strong governance mechanisms to maintain safety, equity, and public trust.<\/jats:p>","DOI":"10.1093\/bjr\/tqag053","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T07:16:38Z","timestamp":1772694998000},"page":"609-627","source":"Crossref","is-referenced-by-count":5,"title":["Artificial intelligence in mammography screening: a narrative review of progress, pitfalls, and potential"],"prefix":"10.1093","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8472-4046","authenticated-orcid":false,"given":"Marina J","family":"Corines","sequence":"first","affiliation":[{"name":"Department of Radiology, Weill Cornell Medicine , New York, NY 10065,","place":["United 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