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Artificial intelligence for postpartum hemorrhage: a systematic review.

Rawan AlSaad¹, Farah Yazbek¹, Thomas Farrell^2,3

¹AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

Frontiers in Global Women'S Health

|June 29, 2026

Summary

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Artificial intelligence (AI) shows promise for predicting postpartum hemorrhage (PPH), but current research relies heavily on retrospective data and lacks robust external validation. Advancing AI for PPH prediction requires multicenter datasets and prospective studies for clinical readiness.

Area of Science:

Obstetrics and Gynecology
Medical Informatics
Artificial Intelligence in Healthcare

Background:

Postpartum hemorrhage (PPH) is a major cause of maternal mortality.
Existing risk tools often miss dynamic intrapartum events.
Artificial intelligence (AI) offers potential for dynamic PPH prediction.

Purpose of the Study:

To systematically review AI models for PPH prediction.
To analyze clinical applications, data sources, and validation strategies.
To assess study quality in AI-based PPH prediction.

Main Methods:

PRISMA-guided systematic review (2015-2025) across major databases.
Independent data extraction and risk of bias assessment using modified QUADAS-2.
Narrative synthesis of findings on study design, AI approaches, and validation.

Keywords:

artificial intelligence hemorrhage maternal obstetrics postpartum pregnancy women's health

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Main Results:

33 studies met criteria, with most published 2023-2025, retrospective, and single-site.
AI models primarily focused on anticipatory risk stratification (79%) for PPH occurrence.
Classical machine learning dominated; deep learning and LLMs were less frequent; validation was mainly internal.

Conclusions:

AI for PPH prediction is expanding but limited by retrospective data and insufficient external validation.
Clinical readiness necessitates harmonized labeling, multicenter data, and prospective evaluation.
Future research should focus on deployment-oriented and prospective studies.