When maternal care—an integral facet of healthcare that involves the health and well-being of mothers and infants—is prioritized, it significantly strengthens families and communities, reduces healthcare costs, and promotes economic and social growth at the national level. Unfortunately, maternal care in rural areas in the US is hard to access, which directly contributes to heightened maternal and infant morbidity and mortality rates. According to a 2025 study, more than 35% of US counties have been observed to be “maternity care deserts,” where women don’t have access to obstetric professionals and birthing facilities. In maternity care deserts, women are more prone to experiencing delayed prenatal care and support and higher maternal mortality.
One way to help improve maternal care in rural areas is the adoption of artificial intelligence (AI) tools that can help healthcare organizations allocate their limited resources appropriately. With AI innovations, such as predictive algorithms and remote, AI-powered monitors, healthcare professionals can make data-driven decisions more efficiently, helping women and infants deal with health-related challenges even from afar. This article explores AI’s frontline impact, from risk prediction to postpartum support.
The Maternal Health Crisis in Rural America
Access to healthcare services in rural areas in the US can be challenging. Based on a 2025 report, more than 10% of pregnant individuals in rural areas live more than 100 miles away from a nearby obstetric facility. This means that these individuals have more difficulty getting prenatal care in the first trimester of their pregnancies compared to those residing in urban areas.
Aside from access to better obstetric resources, those in rural areas are found to consume lower-quality food and supplements compared to their urban counterparts, highlighting how environmental conditions can greatly affect the nutrition pregnant individuals receive. A study that involved 704 pregnant women found that those living in urban environments consumed more vegetables, milk and dairy products, fish, and were given more supplements such as folic acid, even before they became pregnant. Meanwhile, pregnant individuals in rural areas were found to have diets that were of poorer quality.
Because of limited access to quality nutrition and obstetric facilities, pregnant women in rural areas have a higher predicted probability of severe maternal morbidity due to sepsis, pulmonary edema, and acute renal failure. They are also at a higher risk of experiencing postpartum depression and preventable deaths. According to a 2025 study, expectant and new birthing parents in rural areas are almost two times more likely to die than those living in urban environments.
Deploying AI Risk Stratification in Maternity Deserts
AI risk stratification tools for rural maternity deserts can help transform guesswork and limited resources into precision triage and timely care via the following:
AI tools to detect obstetric risks: AI can be used to predict risks among pregnant individuals residing in rural areas, which can result in early diagnosis and well-informed decision-making. A narrative review published in 2025 found that AI tools, such as portable monitoring devices and Clinical Decision Support System (CDSS) using machine learning (ML) algorithms, can effectively detect obstetric risks, such as preeclampsia and preterm birth. A separate study also found that AI models can be used to detect the presence of placenta accreta spectrum (PAS), which is a life-threatening condition that is difficult to detect using existing tools and methods. The use of such tools can help advance feto-maternal health and wellness, enabling pregnant individuals and infants to get lifesaving care, especially those living hundreds of miles away from the nearest obstetric facility.
AI tools to flag gestational diabetes: Gestational diabetes, which affects about 5% to 10% of the pregnant population in the US, can lead to preeclampsia and c-section births, when left unmanaged. Pregestational and gestational diabetes have been observed to skyrocket from 2011 to 2019 in both rural and urban areas. This resulted in adverse pregnancy outcomes, especially for those in rural areas who have limited access to maternal healthcare services. AI can help detect gestational diabetes faster, which can help pregnant individuals get the guidance and care necessary to deliver healthy babies. A 2025 systematic review that involved 126 studies found that AI applications can effectively improve the detection, screening, and management of gestational diabetes.
AI and telehealth: A good way to provide services to underserved and far-flung communities is through telehealth, which allows individuals to seek the care they need in the comfort of their homes, which is especially useful for women and infants in rural areas. With AI tools, healthcare professionals gain a more holistic view of their patients’ overall health through telemedicine. These tools can be used to more accurately diagnose obstetric risks, monitor cardiac health, and provide mental health care for pregnant individuals. AI-powered telehealth services can also provide more accurate and timely consultations, follow-up care, and chronic illness management.
