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INTRODUCTION: Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED: In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION: The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

Original publication

DOI

10.1080/14779072.2023.2223978

Type

Journal article

Journal

Expert Rev Cardiovasc Ther

Publication Date

2023

Volume

21

Pages

531 - 543

Keywords

Hypertension disorders of pregnancy, artificial intelligence, deep learning, machine learning, personalized medicine, preeclampsia, Female, Humans, Pregnancy, Artificial Intelligence, Hypertension, Pregnancy-Induced, Risk Assessment, Delivery of Health Care