AI’s ongoing impact: Implications of AI’s effects on health equity for women’s healthcare providers
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.
Authors
- Elizabeth Wachira (ORCID: https://orcid.org/0000-0003-4219-7545)
- Suman Vadlamani
Institutions
- East Texas A&M University (US)
- The University of Texas Southwestern Medical Center (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Revista Panamericana de Salud Pública
- Published
- 2025-04-09
- DOI
- https://doi.org/10.26633/rpsp.2025.19
- Citations
- 1
- Primary Topic
- Sex and Gender in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.88