Big data and AI for gender equality in health: bias is a big challenge
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 wom...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1436019/full |
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| author | Anagha Joshi Anagha Joshi Anagha Joshi |
| author_facet | Anagha Joshi Anagha Joshi Anagha Joshi |
| author_sort | Anagha Joshi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2159675a466a4791ac12ea59bc31c906 |
| institution | OA Journals |
| issn | 2624-909X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-2159675a466a4791ac12ea59bc31c9062025-08-20T01:47:37ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2024-10-01710.3389/fdata.2024.14360191436019Big data and AI for gender equality in health: bias is a big challengeAnagha Joshi0Anagha Joshi1Anagha Joshi2Computational Biology Unit, Department of Clinical Science, University of Bergen, Bergen, NorwayDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, IndiaCenter for Integrative Biology and Systems Medicine, Wadhwani School of Data Science & Artificial Intelligence, IIT Madras, Chennai, IndiaArtificial 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.https://www.frontiersin.org/articles/10.3389/fdata.2024.1436019/fullwomen's healthsex and gendermachine learningartificial intelligencebiomarkersbias |
| spellingShingle | Anagha Joshi Anagha Joshi Anagha Joshi Big data and AI for gender equality in health: bias is a big challenge Frontiers in Big Data women's health sex and gender machine learning artificial intelligence biomarkers bias |
| title | Big data and AI for gender equality in health: bias is a big challenge |
| title_full | Big data and AI for gender equality in health: bias is a big challenge |
| title_fullStr | Big data and AI for gender equality in health: bias is a big challenge |
| title_full_unstemmed | Big data and AI for gender equality in health: bias is a big challenge |
| title_short | Big data and AI for gender equality in health: bias is a big challenge |
| title_sort | big data and ai for gender equality in health bias is a big challenge |
| topic | women's health sex and gender machine learning artificial intelligence biomarkers bias |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1436019/full |
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