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. 2025 Apr 9:49:e19.
doi: 10.26633/RPSP.2025.19. eCollection 2025.

AI's ongoing impact: Implications of AI's effects on health equity for women's healthcare providers

Suman Vadlamani  1 , Elizabeth Wachira  2
Affiliations

AI's ongoing impact: Implications of AI's effects on health equity for women's healthcare providers

Suman Vadlamani et al. Rev Panam Salud Publica. .

Abstract
in English, Spanish, Portuguese

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.

Objetivo: Evaluar los efectos que el uso actual de la inteligencia artificial (IA) en la salud de las mujeres tiene sobre la equidad en la salud, específicamente en las actividades de prevención primaria y secundaria en las mujeres.

Método: Para realizar esta revisión narrativa se utilizaron dos bases de datos, Scopus y PubMed. En la búsqueda se utilizó el equivalente en inglés de algunas palabras clave como “inteligencia artificial”, “aprendizaje automático”, “salud de la mujer”, “tamizaje”, “factor de riesgo” y “prevenir”, y los artículos solo se filtraron para incluir los que trataban sobre modelos de IA que los médicos generales podrían utilizar.

Resultados: De los 18 artículos examinados, 8 se centraron en la modelización de factores de riesgo en el marco de la prevención primaria y 10 se centraron en las herramientas de tamizaje en el marco de la prevención secundaria. Se encontraron brechas en la capacidad para entrenar a los modelos de IA con conjuntos de datos amplios y diversos que reflejen la población a la que están destinados. La falta de estos conjuntos de datos se detectó con frecuencia como una limitación en los artículos examinados (n = 7).

Conclusiones: Las mujeres pertenecientes a grupos minoritarios y de ingresos bajos tienen poco acceso a la atención de salud y, por lo tanto, no están bien representadas en los conjuntos de datos que se utilizan para entrenar los modelos de AI, lo que podría introducir sesgos en sus resultados. Para mitigar esto, deben crearse más conjuntos de datos para validar los modelos de IA, y la IA en la salud de las mujeres debe ampliarse para incluir las afecciones que afectan a hombres y mujeres, a fin de proporcionar una perspectiva de género al respecto. Las entidades de salud pública, medicina y tecnología deben colaborar para regular el desarrollo y el uso de la IA en la atención de salud de una manera estandarizada que reduzca los sesgos.

Objetivo: Avaliar os efeitos do atual uso da inteligência artificial (IA) na área de saúde da mulher sobre a equidade em saúde, especificamente em atividades de prevenção primária e secundária direcionadas para mulheres.

Métodos: Revisão narrativa de artigos indexados em duas bases de dados, Scopus e PubMed. As palavras-chave incluíram “artificial intelligence”, “machine learning”, “women’s health”, “screen”, “risk factor” e “prevent”, e os artigos foram filtrados de modo a incluir somente artigos sobre modelos de IA para uso por médicos generalistas.

Resultados: Dos 18 artigos examinados, 8 se concentraram na modelagem de fatores de risco na prevenção primária e 10, em ferramentas de rastreamento na prevenção secundária. Foram constatadas lacunas na capacidade de treinar os modelos de IA com conjuntos de dados grandes e diversificados que reflitam as populações às quais se destinam. A falta de tais conjuntos de dados foi frequentemente identificada como uma limitação nos artigos examinados (n = 7).

Conclusões: Mulheres minoritárias e de baixa renda têm acesso limitado à atenção à saúde e, portanto, estão sub-representadas nos conjuntos de dados utilizados para treinamento de modelos de IA, o que gera o risco da introdução de viés nos resultados. Para mitigar isso, é preciso desenvolver mais conjuntos de dados para validar os modelos de IA. Além disso, o uso da IA em saúde da mulher deve ser expandido para incluir afecções que afetem homens e mulheres, de modo a proporcionar uma perspectiva de gênero sobre essas afecções. As entidades de saúde pública, medicina e tecnologia precisam colaborar para regulamentar o desenvolvimento e o uso da IA na atenção à saúde de maneira a reduzir o viés.

Keywords: Artificial intelligence; ethics; primary prevention; secondary prevention; women’s health.

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Conflict of interest statement

Conflict of interest. None declared.

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References

    1. World Health Organization . Geneva: WHO; 2021. Ethics and governance of artificial intelligence for health: WHO guidance. Available from: https://iris.who.int//handle/10665/341996.
    2. World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: WHO; 2021. Available from: https://iris.who.int//handle/10665/341996.
    1. Wolters Kluwer . Alphen aan den Rijn: Wolters Kluwer; 2024. Apr 16, [cited 2024 Nov 6]. Over two-thirds of U.S. physicians have changed their mind, now viewing GenAI as beneficial in healthcare. Available from: https://www.wolterskluwer.com/en/news/gen-ai-clincian-survey-press-release.
    2. Wolters Kluwer. Over two-thirds of U.S. physicians have changed their mind, now viewing GenAI as beneficial in healthcare. [Alphen aan den Rijn]: Wolters Kluwer; 2024 Apr 16 [cited 2024 Nov 6]. Available from: https://www.wolterskluwer.com/en/news/gen-ai-clincian-survey-press-release.
    1. IBM . Armonk, NY: IBM; 2024?. [cited 2024 Sept 23]. What is machine learning (ML)? Available from: https://www.ibm.com/topics/machine-learning.
    2. IBM. What is machine learning (ML)?. [Armonk, NY]: IBM; [2024?] [cited 2024 Sept 23]. Available from: https://www.ibm.com/topics/machine-learning.
    1. U.S. Centers for Disease Control and Prevention . Atlanta, GA: CDC; 2024. [cited 2024 Sept 23]. Social determinants of health (SDOH) Available from: https://www.cdc.gov/about/priorities/why-is-addressing-sdoh-important.html.
    2. U.S. Centers for Disease Control and Prevention. Social determinants of health (SDOH) Atlanta, GA: CDC; 2024 [cited 2024 Sept 23]. Available from: https://www.cdc.gov/about/priorities/why-is-addressing-sdoh-important.html.
    1. American Psychological Association . Washington, D.C.: APA; 2010. [cited 2024 Sept 15]. Fact sheet: Women & socioeconomic status. Available from: https://www.apa.org/pi/ses/resources/publications/women.
    2. American Psychological Association. Fact sheet: Women & socioeconomic status. Washington, D.C.: APA; 2010 [cited 2024 Sept 15]. Available from: https://www.apa.org/pi/ses/resources/publications/women.

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