Artificial Intelligence and Personalized Medicine in Preeclampsia

This book examines preeclampsia as a heterogeneous, multisystem, placenta-associated disorder with major maternal, fetal, and neonatal consequences. It reviews the clinical, epidemiological, and pathophysiological foundations of the disease, then explores biomarkers, omics, imaging, electronic health records, and wearable technologies as data sources for precision medicine. It introduces artificial intelligence, machine learning, deep learning, and explainable AI, emphasizing data quality, external validation, bias, reproducibility, and clinical interpretability. The book discusses how AI may support early prediction, phenotyping, risk stratification, decision support, individualized surveillance, and optimization of hospitalization or delivery timing. It also addresses maternal-fetal outcomes, early warning systems, ethics, regulation, implementation, federated learning, digital twins, and continuous monitoring, offering an integrated framework for personalized maternal-fetal medicine.