Researchers warn that the increasing use of artificial intelligence in women’s health could reinforce existing gender and sex inequities, according to an Aug. 4 perspective article published in npj Women’s Health. The authors say that while AI is often promoted as a solution for personalized care and improved health outcomes, uncritical adoption of these technologies may deepen the very inequalities they aim to address.
The article highlights ongoing challenges, noting that women, transgender, and gender-expansive people continue to receive poorer healthcare and experience worse outcomes compared to other groups. Recent executive orders in the United States have further restricted research into gender, sex, and health disparities by enforcing binary definitions of these categories.
According to the authors, "These restrictions impose a simplistic, binary view of gender and sex and restrict research into their complexities." They caution that treating AI as a universal fix risks overshadowing deeper systemic issues such as reductive research categories and expanding data surveillance.
The paper critiques mainstream medical research for relying on narrow definitions of gender and sex—often defaulting to binary categories—and points out policies like the National Institutes of Health Sex as a Biological Variable policy require reporting by male or female only. The authors argue this approach excludes diverse identities and can lead AI systems to encode outdated assumptions about biological differences.
"AI is frequently promoted as a powerful solution in medical research, but its definition remains vague, and its capabilities are often overstated," the article says. Machine learning models used for identifying differences between sexes can perpetuate flawed assumptions if not carefully designed. Attempts to reduce bias may actually entrench divisions when researchers do not question underlying categories or origins.
The perspective also raises concerns about privacy, with many AI-powered apps depending on extensive surveillance or data extraction from users. The focus on technological innovation can shift attention away from needed structural changes in healthcare delivery. Globally, some AI tools marketed under 'AI for social good' extract data from vulnerable populations without necessarily improving public health outcomes.
The authors recommend involving affected communities throughout all stages of developing AI tools for women’s health—from defining problems through collecting data—to ensure representative inclusion without reinforcing harmful stereotypes or increasing surveillance risks. They conclude: "Meaningful progress in health equity requires a broader commitment to healthcare for all."
