Artificial intelligence can be useful in gynecologic and obstetric emergencies.

This article explores how the mathematics education and educators at the New York, USA women’s college, Barnard College, during its early years approached the issue of equal study on separate campuses as well as how some of the female students of Barnard with a mathematics focus in their studies utilized their knowledge of this male-dominated field to pursue careers following their graduation.

Endometriosis may also affect women of post-reproductive age.

Sexual health and wellbeing are vital components of a woman's life, but older women are often reluctant to discuss sexual issues with healthcare professionals.

The authors suggest that being denied a desired contraceptive method (downward coercion) may have a more profound negative impact on patients' self-efficacy, self-esteem, and reproductive autonomy. Patient autonomy needs to be safeguarded

The study aims to investigate whether machine learning-based predictive models for cardiovascular disease (CVD) risk assessment show equivalent performance across demographic groups (such as race and gender) and if bias mitigation methods can reduce any bias present in the models. This is important as systematic bias may be introduced when collecting and preprocessing health data, which could affect the performance of the models on certain demographic sub-cohorts.

Academic Medical Centers play a key role in providing comprehensive abortion services, from primary care to complex specialty care, and in training future clinicians in abortion provision. The weekly number of abortions at University of Washington remained stable after Dobbs.

This special issue focuses on Women's cardiovascular health, and this study looks at midlife women who have metabolic syndrome.

A study of maternal genetics, milk composition and the infant faecal microbiome. The results show how an improved understanding of the genetics and genomics of human milk connects lactation biology with maternal and infant health.

The study proposes a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment.