{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:15:19Z","timestamp":1784888119631,"version":"3.55.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>Artificial intelligence and machine learning are rapidly evolving fields that have the potential to transform women's health by improving diagnostic accuracy, personalizing treatment plans, and building predictive models of disease progression leading to preventive care. Three categories of women's health issues are discussed where machine learning can facilitate accessible, affordable, personalized, and evidence-based healthcare. In this perspective, firstly the promise of big data and machine learning applications in the context of women's health is elaborated. Despite these promises, machine learning applications are not widely adapted in clinical care due to many issues including ethical concerns, patient privacy, informed consent, algorithmic biases, data quality and availability, and education and training of health care professionals. In the medical field, discrimination against women has a long history. Machine learning implicitly carries biases in the data. Thus, despite the fact that machine learning has the potential to improve some aspects of women's health, it can also reinforce sex and gender biases. Advanced machine learning tools blindly integrated without properly understanding and correcting for socio-cultural sex and gender biased practices and policies is therefore unlikely to result in sex and gender equality in health.<\/jats:p>","DOI":"10.3389\/fdata.2024.1436019","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T05:30:32Z","timestamp":1729056632000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Big data and AI for gender equality in health: bias is a big challenge"],"prefix":"10.3389","volume":"7","author":[{"given":"Anagha","family":"Joshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e050450","DOI":"10.1136\/bmjopen-2021-050450","article-title":"Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data","volume":"12","author":"Azizi","year":"2022","journal-title":"BMJ Open"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1016\/S0140-6736(86)91340-1","article-title":"Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales","volume":"327","author":"Barker","year":"1986","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"780389","DOI":"10.3389\/fbioe.2021.780389","article-title":"Using machine learning to predict complications in pregnancy: a systematic review","volume":"9","author":"Bertini","year":"2022","journal-title":"Front. 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