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Related Experiment Video

Updated: Sep 16, 2025

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Artificial Intelligence and Postpartum Hemorrhage.

Sam J Mathewlynn1,2, Mohammadreza Soltaninejad1,3, Sally L Collins1

  • 1Nuffield Department of Women's and Reproductive Health, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, United Kingdom.

Maternal-Fetal Medicine (Wolters Kluwer Health, Inc.)
|July 7, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in predicting postpartum hemorrhage (PPH). However, more research is needed to validate AI models and ensure their applicability in diverse global healthcare settings, especially in low-resource areas.

Keywords:
Artificial intelligenceDeep learningMachine learningPostpartum hemorrhageRisk prediction

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Maternal Health

Background:

  • Postpartum hemorrhage (PPH) is a major cause of maternal mortality globally.
  • Despite advancements, PPH remains a significant challenge, even in developed countries.
  • Artificial intelligence (AI) is increasingly explored for healthcare applications.

Purpose of the Study:

  • To explore the application of AI in predicting and managing postpartum hemorrhage (PPH).
  • To review current AI models for PPH risk stratification and identify limitations.
  • To highlight future research directions for AI in PPH management.

Main Methods:

  • Review of existing studies on AI applications in PPH prediction and management.
  • Analysis of AI model performance, validation challenges, and applicability in different settings.
  • Exploration of novel AI approaches, including uterine contractility and radiomics.

Main Results:

  • Some AI models show promising results in predicting PPH, but external validation is often lacking.
  • Current research is predominantly from well-resourced settings, with limited models for resource-limited areas.
  • AI is also being investigated for blood product management and early detection via wearables.

Conclusions:

  • AI offers significant potential for improving PPH prediction and management.
  • Key challenges include model validation, clinical translation, and ensuring applicability across diverse healthcare systems.
  • Further research, particularly in low- and middle-income countries, is essential to harness AI's full potential for global PPH reduction.