IMPACT OF ARTIFICIAL INTELLIGENCE ON PREDICTION OF PREECLAMPSIA: HOW MACHINE LEARNING ALGORITHMS CAN ANTICIPATE DIAGNOSIS COMPARED TO TRADITIONAL METHODS

Abstract

Preeclampsia represents a significant obstetric challenge, linked to high rates of complications and fatalities for both mothers and newborns. Conventional screening approaches, including blood pressure checks and lab analyses, have limitations in terms of sensitivity and specificity, which can result in diagnoses being made too late. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) technologies arise as novel solutions that can combine various clinical, laboratory, and genetic data to enhance diagnostic precision and expedite detection. Recent research indicates that AI systems can achieve prediction accuracies exceeding 85% for preeclampsia, outperforming traditional techniques. Additionally, experiences from hospitals in Brazil, like Santa Joana, illustrate the effective use of AI in practice, showcasing a decrease in severe case occurrences and reductions in maternal admissions to intensive care units. Looking ahead, the role of AI in prenatal care is expected to grow, transforming obstetric practices and aiding in lowering both maternal and infant mortality rates.