Women’s Health Innovations Considering the AI Data Lifecycle: Menopause as a Use Case (Preprint)

UNSTRUCTURED National interest has come to the public attention regarding women’s health care, energized by the prevalence of artificial intelligence (AI), machine learning (ML) and other computational tools. Workshops sponsored by the National Science Foundation (NSF) and the National Institutes of Health (NIH) have brought the scientific community together to tackle these challenges head on. Novel advanced computing methods, such as AI and ML and large language models (LLMs), are being optimized to assist biomedical researchers and medical professionals by providing tools for exploring the complexities of care. While AI and ML can enable breakthroughs to address health conditions, future direction and forthcoming initiatives can best serve women’s health challenges by addressing the data gaps present. The limiting factor in data analysis is not algorithms but the lack of representative, longitudinal, multimodal data and clear evaluation frameworks. We propose a complementary line of research in understanding the complexities in healthcare data and clinical workflow. The incomplete nature of these datasets is a stark contrast to other areas of public health, such as infectious disease surveillance and health system performance, where open data has helped improve policies. Without a doubt, there is much promise and peril associated with the growth of generative AI and ML in health care, in general, and women’s health, in particular. The National Academies of Sciences, Engineering and Medicine (NASEM) released a comprehensive report calling for the NIH to re-prioritize its research on women’s health. Currently, NIH does not support an Institute or Center focused on women’s health research nor is there an interdisciplinary funding mechanism .1 Despite these observations, female-specific conditions, including premenstrual syndrome, menopause, maternal hypertensive disorder, warrant critical attention, access to innovation and comprehensive data and modeling, if the women’s health ecosystem will improve health across the lifespan and health span. The ecosystem is an interconnected network of stakeholders, resources, and interventions, and we are poised to use technology to improve the well-being of women across their health and lifespans.