Session: Towards Equitable and Just Applications of AI/ML in Healthcare
Utilizing machine learning to advance maternal health outcomes among Black women: ethical and equity considerations
Friday, October 13, 2023
9:30 AM – 10:45 AM ET
Location: Iron (Fourth Floor)
Maternal death rates and inequities among Black women in the United States are well-documented and steadily rising. Importantly, racial disparities in reproductive and maternal health care are evident during labor and delivery, which has led to higher rates of cesarean deliveries among Black women, heightening the risk of maternal and fetal complications. To foster maternal and reproductive health justice, innovative tools are urgently needed. Machine learning, a form of artificial intelligence, has the potential to prevent the overuse of cesarean deliveries and related complications by predicting when a cesarean delivery is clinically necessary. Algorithmic models can be incorporated into electronic health records to help clinicians make medical decisions beyond known risk factors through ongoing evaluation of clinical parameters. While utilizing this approach, ethical issues can arise such as algorithmic bias, which is more likely to occur if algorithms are not derived in collaboration with diverse stakeholders and experts and regularly evaluated for bias introduction. To optimize the use of machine learning and reduce racial bias, it is critically important to prioritize equity and ethical questions in the development of algorithms. This requires acknowledging anti-Black racism and other structural injustices in health care that prevent Black women from achieving optimal and just maternal care. Used purposefully, machine learning can determine when a cesarean delivery is medically necessary regardless of race, ethnicity, or other identities. However, without centering health equity in the design of clinical care algorithms, this technology may exacerbate health disparities.
Faith Fletcher, PhD, MA – Center for Medical Ethics and Health Policy – Baylor College of Medicine