AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers
Objective. To assess the effects of the current use of artificial intelligence (AI) in women’s health on health equity, specifically in primary and secondary prevention efforts among women. Methods. Two databases, Scopus and PubMed, were used to conduct this narrative review. The keywords included “...
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| Format: | Article |
| Language: | English |
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Pan American Health Organization
2025-04-01
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| Series: | Revista Panamericana de Salud Pública |
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| Online Access: | https://iris.paho.org/handle/10665.2/65979 |
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| _version_ | 1850217667811606528 |
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| author | Suman Vadlamani Elizabeth Wachira |
| author_facet | Suman Vadlamani Elizabeth Wachira |
| author_sort | Suman Vadlamani |
| collection | DOAJ |
| description | Objective. To assess the effects of the current use of artificial intelligence (AI) in women’s health on health equity, specifically in primary and secondary prevention efforts among women.
Methods. Two databases, Scopus and PubMed, were used to conduct this narrative review. The keywords included “artificial intelligence,” “machine learning,” “women’s health,” “screen,” “risk factor,” and “prevent,” and papers were filtered only to include those about AI models that general practitioners may use.
Results. Of the 18 articles reviewed, 8 articles focused on risk factor modeling under primary prevention, and 10 articles focused on screening tools under secondary prevention. Gaps were found in the ability of AI models to train using large, diverse datasets that were reflective of the population it is intended for. Lack of these datasets was frequently identified as a limitation in the papers reviewed (n = 7).
Conclusions. Minority, low-income women have poor access to health care and are, therefore, not well represented in the datasets AI uses to train, which risks introducing bias in its output. To mitigate this, more datasets should be developed to validate AI models, and AI in women’s health should expand to include conditions that affect men and women to provide a gendered lens on these conditions. Public health, medical, and technology entities need to collaborate to regulate the development and use of AI in health care at a standard that reduces bias. |
| format | Article |
| id | doaj-art-d809856caba14718b77cdebed008364e |
| institution | OA Journals |
| issn | 1020-4989 1680-5348 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Pan American Health Organization |
| record_format | Article |
| series | Revista Panamericana de Salud Pública |
| spelling | doaj-art-d809856caba14718b77cdebed008364e2025-08-20T02:07:59ZengPan American Health OrganizationRevista Panamericana de Salud Pública1020-49891680-53482025-04-0149191910.26633/RPSP.2025.19rpspAI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providersSuman Vadlamani0Elizabeth Wachira1University of Texas Health Science Center at Houston, Houston, TX, United States of AmericaEast Texas A&M University, Commerce, TX, United States of AmericaObjective. To assess the effects of the current use of artificial intelligence (AI) in women’s health on health equity, specifically in primary and secondary prevention efforts among women. Methods. Two databases, Scopus and PubMed, were used to conduct this narrative review. The keywords included “artificial intelligence,” “machine learning,” “women’s health,” “screen,” “risk factor,” and “prevent,” and papers were filtered only to include those about AI models that general practitioners may use. Results. Of the 18 articles reviewed, 8 articles focused on risk factor modeling under primary prevention, and 10 articles focused on screening tools under secondary prevention. Gaps were found in the ability of AI models to train using large, diverse datasets that were reflective of the population it is intended for. Lack of these datasets was frequently identified as a limitation in the papers reviewed (n = 7). Conclusions. Minority, low-income women have poor access to health care and are, therefore, not well represented in the datasets AI uses to train, which risks introducing bias in its output. To mitigate this, more datasets should be developed to validate AI models, and AI in women’s health should expand to include conditions that affect men and women to provide a gendered lens on these conditions. Public health, medical, and technology entities need to collaborate to regulate the development and use of AI in health care at a standard that reduces bias.https://iris.paho.org/handle/10665.2/65979artificial intelligencewomen’s healthprimary preventionsecondary preventionethics |
| spellingShingle | Suman Vadlamani Elizabeth Wachira AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers Revista Panamericana de Salud Pública artificial intelligence women’s health primary prevention secondary prevention ethics |
| title | AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers |
| title_full | AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers |
| title_fullStr | AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers |
| title_full_unstemmed | AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers |
| title_short | AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers |
| title_sort | ai s ongoing impact implications of ai s effects on health equity for women s healthcare providers |
| topic | artificial intelligence women’s health primary prevention secondary prevention ethics |
| url | https://iris.paho.org/handle/10665.2/65979 |
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