Article

Leveraging Foundation Models in Maternal and Child Health: A Systematic Review

2025-09-04

Abstract excerpt

Maternal and child health (MCH) represents a critical domain requiring accurate, timely, and data-driven decision-making to optimize outcomes from pregnancy through early childhood. Foundation models (FMs) are large pre-trained artificial intelligence models that offer potential for clinical support in diagnostics, medical adherence, and reducing disparities. We conducted a systematic review to identify recent stu...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 2a035fa1-8edf-521c-aa0c-d476cc4929d9
DOI: 10.1101/2025.08.29.25333768

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Leveraging Foundation Models in Maternal and Child Health: A Systematic ReviewDOI 10.1101/2025.08.29.25333768Read

Advancements in Multimodal Foundation Models for Healthcare: An In-Depth Review and Future OutlookSemantic proximity Read

Application of Foundation Models in Emergency and Critical Care: A Scoping ReviewSemantic proximity Read

Integrating Multimodal Data with Large Foundation Models in HealthcareSemantic proximity Read

Multi-Model Clinical Validation of an AI-Powered Biomarker Analysis Framework: A Cross-Vendor Benchmark on 4,018 NHANES PatientsSemantic proximity Read

Artificial Intelligence in Healthcare: 2024 Year in ReviewSemantic proximity Read

Artificial Intelligence in Healthcare: 2025 Year in ReviewSemantic proximity Read

Human-supervised, large language model-based clinical decision support aligned to national newborn protocols in Kenya: a pragmatic, early-stage evaluationSemantic proximity Read

A Systematic Review and Implementation Guidelines of Multimodal Foundation Models in Medical ImagingSemantic proximity Read

Adaptive Agentic AI-Driven Women's Digital Twin for Personalized High-Risk Pregnancy Prediction and Clinical Decision SupportSemantic proximity Read

What it takes to implement AI in Africa: health-system lessons from developing an ML-enabled maternal risk stratification algorithm in TanzaniaSemantic proximity Read

AI decision-making performance in Maternal-Fetal Medicine compared with human specialists: a cross-sectional studySemantic proximity Read

Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenSemantic proximity Read