Evidence map›Paper›PMID 40850917›Full record

ArticleBMJ open2025

From pilot to practice: a scoping review protocol mapping the development of AI-enabled solutions for maternal health using technology readiness levels.

Nico Marquardt, Vladimir Choi, Charles Martyn-Dickens, Marelize Gorgens, Sam Mathewlynn, Tobias Kurth, Philipp Bouteiller, Lothar H Wieler

Abstract read
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nico MarquardtInstitute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany nico.marquardt@charite.de.ORCID http://orcid.org/0009-0001-7363-2055
Vladimir ChoiFree University of Berlin, Berlin, Germany.
Charles Martyn-DickensKomfo Anokye Teaching Hospital, Kumasi, Ghana.
Marelize GorgensWorld Bank, Washington, District of Columbia, USA.
Sam MathewlynnNuffield Dept. of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Tobias KurthInstitute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0001-7169-2620
Philipp BouteillerXU Exponential University of Applied Sciences, Potsdam, Germany.
Lothar H WielerDigital Global Public Health, Hasso-Plattner-Institut, University Potsdam, Potsdam, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMaternal mortality remains a critical public health challenge in low- and middle-income countries (LMICs), where over 92% of global maternal deaths occur. Artificial intelligence (AI)-enabled solutions are increasingly recognised for their potential to improve and expand health services delivered to women. Such solutions can accelerate how health systems address gaps in maternal healthcare, including prevention, early detection, intervention and treatment. However, the extent to which AI-enabled solutions have progressed toward real-world application in LMIC healthcare systems remains unclear. This scoping review aims to systematically map the development of AI-enabled solutions for maternal health by applying the Technology Readiness Level (TRL) framework to assess their stage of advancement. It also aims to identify facilitators, barriers and critical research gaps. METHODS AND ANALYSIS: This scoping review will be guided by established methodological frameworks and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. A comprehensive literature search will be performed across PubMed, EMBASE, IEEE Xplore, CINAHL, CABI and Scopus, as well as grey literature sources. The search will combine controlled vocabulary and keywords related to 'artificial intelligence' and 'maternal health'. Studies reporting any AI-enabled solutions in maternal health, specifically targeting the leading direct causes of maternal mortality (eg, postpartum haemorrhage, hypertensive disorders, sepsis, delivery complications and unsafe abortion), published between 1 January 2015 and 1 August 2025, will be eligible. Two independent reviewers will screen studies, chart relevant data and resolve discrepancies through consensus. Findings will be synthesised using a narrative and tabular approach to map the extent and characteristics of the literature. ETHICS AND DISSEMINATION: Ethical approval is not required as the review involves analysis of publicly available data. The findings will be disseminated through publication in a peer-reviewed journal and presentations at relevant conferences.

Indexed as

Artificial IntelligenceMaternal HealthMaternal Health ServicesFemaleHumansMaternal MortalityPregnancyResearch DesignScoping Reviews as TopicArtificial IntelligenceMachine LearningOBSTETRICS

Identifiers

PMID40850917
PMCPMC12382553

What OpenQuestion holds

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Registered trials

None linked

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.