AI’s ongoing impact
Implications of AI’s effects on health equity for women’s healthcare providers
Bibliographic Data
| ID | 14968885 |
|---|---|
| Authors | Suman Vadlamani (University of Texas Health Science Center at Houston, Houston, TX, United States of America, corresponding author), Elizabeth Wachira (0000-0003-4219-7545, East Texas A&M University, Commerce, TX, United States of America) |
| Year | 2025 |
| Volume | 49 |
| Pages | 1 |
| Publication date | 2025-04-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Revista Panamericana de Salud Pública (JOURNAL) |
| Journal identifiers | ISSN: 1020-4989 • E-ISSN: 1680-5348 |
| Publisher | Pan American Health Organization (PUBLISHER • US) |
| DOI | 10.26633/rpsp.2025.19 |
| PMID | 40206564 |
| OpenAlex | W4409292519 |
| Language | EN |
| References cited | 25 |
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
Environmental health · Equity (law · Health care · Health equity · MEDLINE · Political science · Population · Public health · Scopus · Artificial Intelligence in Healthcare and Education · Computer Science · Diversity and Career in Medicine · Medicine · Nursing · Psychology · Sex and Gender in Healthcare · Artificial Intelligence · Gerontology
| Highly cited | No |
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