The role of Artificial Intelligence in Improving Maternal Fetal Health Outcomes: A Mini Review

Abstract

Maternal-fetal health is a vital area of healthcare that significantly impacts the long-term wellbeing of mothers and their children. Despite advancements in medical technology, maternal and neonatal morbidity and mortality remain critical global health challenges. Complications such as preterm birth, preeclampsia, gestational diabetes, and fetal growth restrictions contribute to adverse outcomes and highlight the need for innovative solutions. Artificial Intelligence (AI) offers promising opportunities to address these challenges by enhancing diagnostic accuracy, predictive analytics, and personalized treatment plans. AI technologies, including machine learning, deep learning, natural language processing, and computer vision, can analyze vast datasets from electronic health records, wearable devices, genetic information, and imaging studies. These technologies enable early detection of complications, more precise diagnostic imaging, and real-time fetal monitoring. Predictive models powered by AI can identify high-risk pregnancies and provide healthcare providers with valuable insights for early intervention. AI also facilitates the creation of personalized care plans, optimizing management of conditions like gestational diabetes through customized dietary and exercise recommendations, glucose monitoring, and medication management. Despite the potential benefits, several challenges and ethical considerations must be addressed, including data privacy, algorithmic bias, and the need for robust regulatory frameworks. By navigating these challenges and leveraging AI's capabilities, maternal-fetal health can be transformed, improving outcomes and reducing disparities in care. This review explores the current applications and future prospects of AI in maternal-fetal health, emphasizing the importance of ethical and equitable implementation to fully realize AI's potential in this critical field.