Integrated artificial intelligence and omics for prediction and monitoring of pre-eclampsia

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — no pooled effect size or diagnostic accuracy metrics (e.g., AUC, sensitivity, specificity) are synthesised across studies

• Primary Outcome: No quantitative primary outcome is reported. The review narratively describes AI applications for early detection and severity prediction of pre-eclampsia using clinical, biochemical, and imaging data.

• Nnt Or Sensitivity: Not reported. Red cell distribution width is described qualitatively as a cost-effective inflammatory marker; no sensitivity, specificity, or likelihood ratios are provided for any biomarker or AI model discussed.

• Confidence Interval: Not reported

Clinical Application

Clinical feasibility of the described AI and omics approaches is not formally assessed. Wearable devices, cloud-based imaging systems, and multi-omics platforms remain largely experimental or research-stage tools. Integration into routine antenatal care would require regulatory approval, clinical validation in prospective trials, infrastructure investment, and clinician training. Cost-effectiveness analyses are absent. Pre-eclampsia affects approximately 5–8% of pregnancies in Australia and is a priority area for the RANZCOG and RACGP. The TGA would need to evaluate and approve any AI-based diagnostic device before clinical deployment. No AI-based pre-eclampsia prediction tool is currently listed on the Australian Register of Therapeutic Goods (ARTG) for routine clinical use. The PBS does not currently subsidise multi-omics panels for pre-eclampsia screening. Australian guidelines (RANZCOG) recommend aspirin prophylaxis for high-risk women and uterine artery Doppler screening — AI integration into these pathways is not yet supported by sufficient evidence. The review's discussion of low-resource applicability is relevant to remote and Indigenous Australian communities where access to specialist obstetric care is limited, but implementation barriers are not addressed. Pregnant women at risk of pre-eclampsia, particularly in settings where early detection and continuous monitoring are limited. The review suggests potential utility across both high-resource and low-resource clinical environments, though this is not empirically validated within the paper.

Abstract

Pre-eclampsia is a difficult pregnancy condition that causes high blood pressure and can lead to health complications in both mother and newborn, resulting in a higher fatality rate. It presents with a wide range of symptoms and lacks specific indicators, as the contemporary diagnostic techniques, including proteinuria testing and blood pressure measurements, are not reliable. The current evolution in artificial intelligence (AI) technology tends to show a promising transformation of pre-eclampsia management. AI algorithms are applied to process larger sets of clinical, biochemical, and image data that facilitate timely medical interventions by bringing up the early-onset and severity of pre-eclampsia. By analyzing the red cell distribution width (blood test indicators for pre-eclampsia), it is recognized as a cost-effective way of detecting inflammation. The application of AI technology on non-invasive diagnostic (wearable) devices enables continuous monitoring with imaging techniques for the placenta and retina via cloud-based systems. These developments are not only applied for early detection of pre-eclampsia, but also assist decision making capabilities in both high- and low-resource environments. This article explains how the growing use of AI is changing the way that pre-eclampsia is understood and managed, with the aim of improving accuracy and offering more personalized care for pregnant women.

References

1. 1.Baiju, V. P., Subash, R., & Venkatesan, N. (2026). Integrated artificial intelligence and omics for prediction and monitoring of pre-eclampsia. International Journal of Gynaecology and Obstetrics: The Official Organ of the International Federation of Gynaecology and Obstetrics. Advance online publication. https://doi.org/10.1002/ijgo.70820