Integrating artificial intelligence and big data analysis could provide personalized recommendations for preventive cardiovascular care in patients with hypertensive disorders of pregnancy.
Can artificial intelligence and big data analysis improve personalized cardiovascular care and risk assessment in patients with hypertensive disorders of pregnancy?
AI and big data analysis offer a promising approach to move beyond simple risk assessments toward personalized cardiovascular care for women with a history of hypertensive disorders of pregnancy.
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.
Alkhodari et al. (Sat,) conducted a review in Hypertensive disorders of pregnancy. Artificial intelligence was evaluated. Integrating artificial intelligence and big data analysis could provide personalized recommendations for preventive cardiovascular care in patients with hypertensive disorders of pregnancy.