Harnessing AI to bridge gaps in women’s health care

Credit: U.S. National Science Foundation

Menopause is a natural stage in life that affects all women as they age. It is often accompanied by physical, physiological, emotional and cognitive changes that can significantly affect health. Nearly 85% of women in the U.S. report symptoms associated with menopause. Despite its widespread impact, menopause has received relatively little attention in biomedical research.

Since menopause often coincides with the development of age-related chronic diseases, understanding the body's physiological changes during this period is crucial. Researchers are turning to advanced technologies like artificial intelligence to enhance risk prediction, prevention and symptom management, addressing both the immediate symptoms of menopause and menopause-related complications like osteoporosis, cardiovascular diseases and cognitive decline.

By analyzing large volumes of data, including genetic markers, hormone levels and behavior, AI is enabling scientists to discover previously undetected variations in women's biological changes. These insights are leading to more accurate diagnoses and enabling the development of personalized treatment strategies, ultimately improving health outcomes and enhancing quality of life.

Credit: Charlotte Geary/NSF

NSF Chief Science Officer Karen Marrongelle addresses an NSF-NIH workshop, "Using Artificial Intelligence to Better Understand Menopause."

Closing gaps in research with AI

Historically, women have been underrepresented in clinical trials, meaning that much of the available data is skewed toward male populations.

AI can address gender disparities in clinical trial data by using techniques like data augmentation and transfer learning to enhance women's representation. It can analyze male-dominated data for applicable patterns, prioritize recruitment strategies for underrepresented groups, and extract insights from smaller datasets, ultimately leading to more equitable research and better health outcomes for women.

However, while AI is crucial for improving women's health research, collaboration is essential to addressing the underrepresentation of women in biomedical studies. For example, The White House's "Executive Order on Advancing Women's Health Research and Innovation" emphasizes the importance of inclusive research efforts, particularly in menopause research, by promoting the development of evidence-based guidelines and improving health data collection.

In alignment with these initiatives, the U.S. National Science Foundation hosted a landmark workshop, "Using AI to Better Understand Menopause," which brought together experts from the biomedical and computing fields. The discussion explored how AI can analyze data to reveal patterns that traditional research methods may miss. Panelists also acknowledged challenges that need to be addressed, including the lack of relevant health data.

"Menopause and perimenopause are so widely experienced, yet so rarely discussed, and too rarely researched," said NSF Chief Science Officer Karen Marrongelle. "In bringing leading experts in women's health together with experts in AI, and designing new programs informed by their insights, NSF is elevating and expanding necessary conversations about the challenges of menopause while accelerating research to find solutions."

Machine learning is a powerful branch of AI that is particularly adept at overcoming challenges when working with skewed or incomplete datasets. It uses advanced algorithms and statistical modeling to train itself with new data, continuously improving the accuracy of its results. By processing and learning from vast and complex datasets, machine learning algorithms can analyze information such as genetic, physiological and behavioral data to identify subtle trends and connections that are not easily detected. Researchers can then focus on this newly uncovered information to further explore how these factors may be impacting women's health.

Transforming women's health through innovation

The workshop is part of a broader effort to advance research addressing the unique health needs of women. Another example is the NSF Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science program, which supports research into the application of AI and machine learning to address women's health issues, including menopause and chronic conditions.

The Engineering Research Visioning Alliance (ERVA) is also dedicated to enhancing women's health through interdisciplinary collaborations and innovation in biomedical engineering. ERVA integrates advanced technologies such as AI, wearable devices and data analytics to address important issues affecting women. ERVA aims to expedite research and development to improve women's health outcomes by developing roadmaps and bringing together experts.

As the global population ages, the demand for better healthcare solutions for pre- and menopausal women will rise. AI breakthroughs have the potential to revolutionize menopause diagnosis and treatment, offering more personalized and effective care. Ultimately, this work is shaping the future of women's well-being, providing tools and treatments for healthier, more fulfilling lives.

To hear more from a panelist who participated in the "Using AI to Better Understand Menopause" workshop, watch this video.

AI and Improving Women’s Health

Credit: U.S. National Science Foundation

Carolyn Mazure, chair of the White House Initiative on Women's Health Research, discusses the important role AI is playing in improving women's health.

About the Author

Tricia Rowlison

Senior Scientific Writer

Tricia is a science writer at the U.S. National Science Foundation where she translates complex scientific research into engaging and accessible content for the public. Her work aims to help audiences understand the significant discoveries and innovations funded by NSF and their societal benefits. Prior to joining NSF, Tricia worked as a postdoctoral researcher at the Smithsonian's National Zoo and Conservation Biology Institute. During this time, she studied reproductive physiology for the conservation of endangered species. Her involvement in animal conservation also provided her with the opportunity to take part in numerous educational outreach programs, igniting her passion for science communication.

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