ReviewPLOS digital health2025
The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.
Review in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Toward resilient and efficient pediatric critical care provision amid falling birth rates.World journal of pediatrics : WJP · 2026Article
- Immunology of pregnancy and sepsis: shared and specific pathways guiding future precision care.EBioMedicine · 2026Review
- Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review.Epidemiologia (Basel, Switzerland) · 2026Review
- Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.PLOS digital health · 2026Article
- The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.Cost effectiveness and resource allocation : C/E · 2026Review
- Artificial intelligence and the future of maternal and newborn health in low-income countries: advancing equity, early detection, and health system resilience.Frontiers in public health · 2026Review
- Can ethical artificial intelligence aid infant and young child feeding support during conflict? A review of the experience and lessons learned from FHI 360's in Ukraine.Frontiers in nutrition · 2026Article
- AI supported diagnostic innovations for impact in global women's health.BMJ (Clinical research ed.) · 2025Article
- Non-Invasive Prenatal Testing (NIPT): A Paradigm Shift in Prenatal Care.International journal of preventive medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This debate paper examines the transformative potential of Artificial Intelligence (AI), specifically through Machine Learning (ML), in enhancing preventive measures in maternal and child health (MCH). With the proliferation of Big Data, ML has become crucial in handling complex, non-linear interactions among health determinants to not only predict but also prevent adverse outcomes. This paper underscores AI's applications in early interventions that could decrease the incidence of MCH issues. It reviews technological advancements while addressing ethical, practical, and data-related challenges in applying AI in preventive healthcare. Emphasis is placed on recent supervised, unsupervised, and reinforcement learning applications that significantly advance preventive care, particularly in low-resource settings. The manuscript discusses the development of AI models for early diagnosis, comprehensive risk assessments, and customized preventive interventions, while highlighting challenges like data diversity, privacy issues, and integrating multimodal health data.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.