Artificial intelligence for postpartum hemorrhage: a systematic review

Postpartum hemorrhage (PPH) remains a leading cause of maternal morbidity and mortality, and conventional risk tools often rely on static factors that may miss rapidly evolving intrapartum events. Artificial intelligence (AI) offers data-driven, potentially dynamic approaches for PPH prediction, yet prior syntheses have provided limited coverage of recent methods and validation practices.

To systematically synthesize the evidence on AI models for PPH prediction, including clinical applications, data sources, prediction targets, validation strategies, and study quality.

We conducted a PRISMA-guided systematic review of studies published between 2015 and 2025 across MEDLINE, Embase, Scopus, IEEE Xplore, and Google Scholar. Two reviewers independently screened records, extracted data using a prespecified form, and resolved disagreements by consensus. Risk of bias and applicability were appraised using a modified QUADAS-2 tool tailored to AI-based PPH prediction. Findings were synthesized narratively across study design, prediction context, inputs, modeling approaches, and validation strategies.

In total, 33 studies met the eligibility criteria. Publications were concentrated in 2023-2025 (79%) and were predominantly retrospective (91%) and single-site (55%). Most models targeted anticipatory risk stratification (79%), with fewer addressing intrapartum/immediate postpartum early warning or severity escalation (24%). Outcomes primarily modeled PPH occurrence (79%) as binary classification (91%). All studies used closed datasets. Classical machine learning dominated (85%), while deep learning (33%) and LLM-based approaches (6%) were less frequent. Validation was mainly internal (76%), with limited external validation. Participant selection was the main quality concern (high risk of bias: 48%), while index test and reference standard were largely low risk.

AI-based PPH prediction research is rapidly expanding but remains constrained by retrospective, closed datasets, heterogeneous outcome definitions, and limited external/temporal validation. Progress toward clinical readiness requires harmonized labeling, multicenter datasets, deployment-oriented evaluation, and prospective implementation studies.