ReviewJournal of multidisciplinary healthcare2026
Artificial Intelligence (AI) Implementation in Maternal and Child Health: A Scoping Review.
Review in Journal of multidisciplinary healthcare, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has increasingly been applied in maternal and child health. However, current evidence remains largely focused on model development, while reports on real-world clinical implementation are limited. This scoping review aimed to map the implementation of AI in maternal and child health settings and summarize reported outcomes. This scoping review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) guideline. Searches were conducted in PubMed and Scopus using Boolean operators combining terms related to artificial intelligence, maternal and child health, and implementation. Studies published in English between 2021 and 2026 were included if they reported clinical or community-based implementation of AI involving real patients or healthcare providers within an ongoing care pathway. Studies focused solely on AI model development or technical validation, reviews, conference abstracts, and editorials were excluded. Of 191 records identified, 156 were screened after duplicate removal, 64 full-text reports were assessed, and seven studies met all inclusion criteria. Identified AI applications were grouped into three themes: maternal support and community-based interventions, neonatal and pediatric monitoring, and screening and diagnostic support. Included studies reported promising outcomes, including improved monitoring accuracy, maternal engagement, and image quality standardization in low-resource settings. However, the seven included studies were highly heterogeneous, and most remained limited to feasibility studies or early-stage implementations. Current evidence suggests AI holds promise as an assistive tool in maternal and child healthcare. However, given the limited number and heterogeneity of included studies, this evidence should be interpreted as preliminary. Organizational, regulatory, financial, and workforce-related barriers, along with ethical considerations such as data privacy and algorithmic bias, remain to be addressed. Further large-scale, long-term implementation studies are needed to evaluate the integration and sustainability of AI in routine maternal and child healthcare practice.
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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.