Novel machine learning applications in peripartum care: a scoping review.

Journal: American journal of obstetrics & gynecology MFM
PMID: 39855597

Abstract

OBJECTIVE: Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate early interventions and improve decision-making. Considering the global challenges of maternal, fetal, and neonatal morbidity and mortality, ML holds the potential to enable significant improvements in maternal and neonatal health outcomes. We aimed to conduct a comprehensive review of ML applications in peripartum care, summarizing the potential of these tools to enhance clinical decision-making and identifying emerging trends and research gaps.

Authors

  • Shani Steinberg
    The Josef Buchmann Gynecology and Maternity Center, Sheba Medical Center (Steinberg, Tsur), Tel Hashomer, Israel; Faculty of Medicine, Tel-Aviv University (Steinberg, Zimlichman, Tsur), Tel-Aviv, Israel. Electronic address: shani.steinberg@gmail.com.
  • Melissa Wong
    Department of MedicineUniversity of California at San Diego La Jolla CA 92092 USA.
  • Eyal Zimlichman
    Sheba Medical Center, Tel Hashomer, Israel.
  • Abraham Tsur
    The Josef Buchmann Gynecology and Maternity Center, Sheba Medical Center (Steinberg, Tsur), Tel Hashomer, Israel; ARC Innovation Center, Sheba Medical Center (Zimlichman, Tsur), Ramat Gan, Israel; Faculty of Medicine, Tel-Aviv University (Steinberg, Zimlichman, Tsur), Tel-Aviv, Israel; The Dina Recanati School of Medicine, Reichmann University (Zimlichman, Tsur), Herzliya, Israel.

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