Abstract

Artificial intelligence (AI) and machine learning (ML) technologies have increasingly become integral in healthcare, offering promising solutions for analyzing complex medical data. This chapter explores the application of AI, particularly ML, in predicting perinatal complications, a crucial aspect of maternal and neonatal health. With advancements in AI, clinicians can better analyze diverse data sources, including electronic health records, medical images, and biomarkers, to predict outcomes such as preeclampsia, prematurity, and neonatal mortality. The emergence of explainable AI further enhances the transparency and interpretability of these models, thereby making it easier for clinicians to understand and trust the predictions. Despite the promising results, challenges remain, particularly in integrating these technologies into clinical practice. Issues like the “black box” nature of some AI models and the need for more comprehensive validation methods are addressed. The chapter highlights the potential for AI to revolutionize women's healthcare by providing more accurate diagnoses, thus reducing physician workload and identifying critical predictive variables that could lead to more personalized and effective treatments. Also, it explores new technologies for fetal health, such as fetal movements. The use of accelerometry is a promising field for remotely monitoring fetal well-being. Intelligent systems employing wearable devices and continuous monitoring techniques can detect complications such as early preeclampsia, thus providing healthcare professionals with understandable and actionable information. By prioritizing explainability, we ensure that models are transparent and understandable, thus fostering their adoption and utility in clinical practice. Ultimately, integrating explainable methods into fetal monitoring and the prediction of perinatal complications can enhance early detection and risk management, thereby contributing to better health outcomes for mothers and newborns.