{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:17:33Z","timestamp":1791893853283,"version":"4.3.4"},"reference-count":95,"publisher":"Oxford University Press (OUP)","issue":"1180","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Women continue to be disproportionately affected by a large burden of disease including cardiovascular disease, cancer, gynecologic disorders, osteoporosis, and maternal health complications contributing to significant morbidity and mortality. Traditional diagnostic tools and risk models often fail to account for sex-specific factors, leading to underdiagnosis and delayed care. Artificial intelligence (AI) is rapidly emerging as a transformative tool in women\u2019s health, offering new methods for opportunistic screening, early detection, and risk prediction across multiple conditions. This review explores the application of AI in radiology imaging with a focus on diseases that only affect women, and those that affect both men and women, focusing on the outcomes for women and how AI is affecting their care. We describe different applications of AI and summarize types of bias affecting these applications, with recommendation on strategies to mitigate these disparities. Neglecting women\u2019s health has profound economic, societal, and global health consequences. We hope by highlighting some transformative AI applications for women\u2019s health, we can promote their adoption to accelerate care, while factoring in various pitfalls that risk leaving women behind in the ongoing AI transformation. Specifically, we recommend a sociotechnical approach to AI development and deployment for women\u2019s health\u2013factoring in the impact of complex social systems that have allowed persistent disparities and underinvestment in women\u2019s health.<\/jats:p>","DOI":"10.1093\/bjr\/tqag035","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T10:17:29Z","timestamp":1774088249000},"page":"669-678","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging artificial intelligence for equitable women\u2019s health outcomes through imaging"],"prefix":"10.1093","volume":"99","author":[{"given":"Brandy","family":"Ndirangu","sequence":"first","affiliation":[{"name":"Emory School of Medicine , Atlanta, GA 30318,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janice","family":"Newsome","sequence":"additional","affiliation":[{"name":"Department of Radiology & Imaging Sciences, Emory University , Atlanta, GA 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