Application of artificial intelligence models trained on multiparametric datasets for predicting pregnancy complications

Aim: to compare the predictive performance of artificial intelligence (AI) and machine learning (ML) models trained on multiparametric datasets for the prediction of preeclampsia (РЕ) and obstetric hemorrhage.

Materials and Methods. This systematic review was conducted in accordance with the PRISMA guidelines. PubMed and Cochrane Central databases were searched for studies published between 2015 and 2025. We included studies applying AI/ML with ≥ 2 predictors/data modalities and reporting outcomes related to РЕ/eclampsia or obstetric hemorrhage (including postpartum hemorrhage). Risk of bias was assessed using Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool and the Newcastle–Ottawa Scale (NOS).

Results. Twenty-eight studies were included (18 on РЕ and 10 on hemorrhage). Most common algorithms were gradient boosting methods, random forests, XGBoost, and neural networks were the most common algorithms. For preeclampsia, stronger performance was more consistently reported when maternal risk factors were combined with blood pressure features and first-trimester screening components (uterine artery Doppler and placental biomarkers). For hemorrhage prediction, models based on electronic health records and preoperative clinical and laboratory variables, including risk stratification in placenta previa/placenta accreta spectrum (PAS), appeared particularly relevant. The evidence base is limited by predominantly retrospective designs and insufficient external/prospective validation, which undermines model transportability across settings and over time.

Conclusion. AI supported by multiparametric monitoring shows promise for predicting РЕ and obstetric hemorrhage; however, real-world implementation requires standardized reporting, external validation, and ongoing calibration monitoring.

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