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Peer-reviewedCC BY-NC-NDBMC medical informatics and decision making

AI-powered risk prediction models for preventable maternal mortality in rural settings: a systematic review.

  • Joy Aifuobhokhan1
  • Ayodeji Ogunjinmi2
  • Chukwuemeka Abraham Agbarakwe3
  • Deborah Oladunmolu Oduguwa4
  • Annie Peter Essiet5
  • Temitayo Osunkiyesi6
  • Akinbogun Modesire7
7 affiliations
  1. 1Lakeshore Cancer Centre, Lagos, Nigeria. [email protected].
  2. 2Bingham University Teaching Hospital, Jos, Nigeria.
  3. 3Calvary Specialist Hospital, Port Harcourt, Nigeria.
  4. 4Ogun State, Babcock University Teaching Hospital, Ilishan-Remo, Nigeria.
  5. 5Tehilah Children's Hospital, Port Harcourt, Nigeria.
  6. 6University of Lagos, Lagos, Nigeria.
  7. 7Babcock University Teaching Hospital, Ilishan-Remo, Nigeria.
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Abstract

Background

Maternal mortality remains disproportionately high in low- and middle-income countries, particularly in rural settings with limited access to skilled obstetric care. Artificial intelligence and machine learning models offer promise for early risk prediction, yet their methodological rigour, applicability, and deployment feasibility in resource-constrained rural contexts remain inadequately synthesised. This systematic review evaluated AI-powered risk prediction models for preventable maternal mortality, with an emphasis on their suitability for rural and low-resource settings.Methods

A systematic literature search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, Google Scholar, and African Journals Online for studies published from January 2015 to August 2025. Studies employing AI or machine learning to predict maternal mortality or severe maternal outcomes were included. The Prediction model Risk Of Bias Assessment Tool (PROBAST) assessed methodological quality across four domains: participants, predictors, outcomes, and analysis. Data extraction captured study characteristics, model architectures, performance metrics, validation strategies, and rural implementation considerations. This review was registered with PROSPERO (CRD420251174343) and reported in accordance with PRISMA 2020 guidelines.Results

Twenty-eight studies met inclusion criteria, predominantly from sub-Saharan Africa (n = 12) and South Asia (n = 8). Dataset sizes ranged from 402 to over 31 million records from national surveys (n = 14), hospital registries (n = 9), and Internet of Things monitoring systems (n = 5). Random Forest (n = 14), ensemble methods (n = 11), and neural networks (n = 11) were most frequently employed. Reported area under the receiver operating characteristic curve values ranged from 0.70 to 0.95 (median 0.84), with sensitivity 70-92% and specificity 65-85%. PROBAST assessment revealed low risk of bias for participants (24/28), predictors (25/28), and outcomes (24/28), but substantial concerns in the analysis domain (14/28 low risk, 8/28 high risk). Key limitations included reliance on synthetic oversampling without external validation, inadequate reporting of calibration, and small sample sizes in IoT studies. Only 11 studies (39%) conducted external validation. Common predictors were maternal age, blood pressure, gestational age, parity, and antenatal care attendance. Rural implementation barriers included limited connectivity, data sparsity, workforce training needs, and the absence of explainability frameworks.Conclusions

AI-powered models demonstrate strong discrimination performance for maternal mortality prediction when trained on large, representative datasets. However, methodological weaknesses, particularly inadequate external validation and calibration assessment, limit confidence in generalizability. The underrepresentation of rural populations and the scarcity of implementation studies constrain real-world applicability. Future development should prioritise federated learning for privacy-preserving multi-site collaboration, lightweight architectures for offline deployment, explainable AI frameworks, and integration with community health worker workflows to achieve equitable, scalable solutions to reduce preventable maternal deaths in rural low- and middle-income country settings.Systematic review registration

PROSPERO CRD42025174343.

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Cite

BibTeXRIS
BibTeX
@article{aifuobhokhan2026aipowered,
  title = {{AI-powered risk prediction models for preventable maternal mortality in rural settings: a systematic review.}},
  author = {Joy Aifuobhokhan and Ayodeji Ogunjinmi and Chukwuemeka Abraham Agbarakwe and Deborah Oladunmolu Oduguwa and Annie Peter Essiet and Temitayo Osunkiyesi and Akinbogun Modesire},
  journal = {BMC medical informatics and decision making},
  year = {2026},
  month = jul,
  doi = {10.1186/s12911-026-03716-6},
  url = {https://europepmc.org/article/MED/42897589},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - AI-powered risk prediction models for preventable maternal mortality in rural settings: a systematic review.
AU  - Joy Aifuobhokhan
AU  - Ayodeji Ogunjinmi
AU  - Chukwuemeka Abraham Agbarakwe
AU  - Deborah Oladunmolu Oduguwa
AU  - Annie Peter Essiet
AU  - Temitayo Osunkiyesi
AU  - Akinbogun Modesire
PY  - 2026
DA  - 2026/07/01
JO  - BMC medical informatics and decision making
DO  - 10.1186/s12911-026-03716-6
UR  - https://europepmc.org/article/MED/42897589
AB  - Background  Maternal mortality remains disproportionately high in low- and middle-income countries, particularly in rural settings with limited access to skilled obstetric care. Artificial intelligence and machine learning models offer promise for early risk prediction, yet their methodological rigour, applicability, and deployment feasibility in resource-constrained rural contexts remain inadequately synthesised. This systematic review evaluated AI-powered risk prediction models for preventable maternal mortality, with an emphasis on their suitability for rural and low-resource settings.Methods  A systematic literature search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, Google Scholar, and African Journals Online for studies published from January 2015 to August 2025. Studies employing AI or machine learning to predict maternal mortality or severe maternal outcomes were included. The Prediction model Risk Of Bias Assessment Tool (PROBAST) assessed methodological quality across four domains: participants, predictors, outcomes, and analysis. Data extraction captured study characteristics, model architectures, performance metrics, validation strategies, and rural implementation considerations. This review was registered with PROSPERO (CRD420251174343) and reported in accordance with PRISMA 2020 guidelines.Results  Twenty-eight studies met inclusion criteria, predominantly from sub-Saharan Africa (n = 12) and South Asia (n = 8). Dataset sizes ranged from 402 to over 31 million records from national surveys (n = 14), hospital registries (n = 9), and Internet of Things monitoring systems (n = 5). Random Forest (n = 14), ensemble methods (n = 11), and neural networks (n = 11) were most frequently employed. Reported area under the receiver operating characteristic curve values ranged from 0.70 to 0.95 (median 0.84), with sensitivity 70-92% and specificity 65-85%. PROBAST assessment revealed low risk of bias for participants (24/28), predictors (25/28), and outcomes (24/28), but substantial concerns in the analysis domain (14/28 low risk, 8/28 high risk). Key limitations included reliance on synthetic oversampling without external validation, inadequate reporting of calibration, and small sample sizes in IoT studies. Only 11 studies (39%) conducted external validation. Common predictors were maternal age, blood pressure, gestational age, parity, and antenatal care attendance. Rural implementation barriers included limited connectivity, data sparsity, workforce training needs, and the absence of explainability frameworks.Conclusions  AI-powered models demonstrate strong discrimination performance for maternal mortality prediction when trained on large, representative datasets. However, methodological weaknesses, particularly inadequate external validation and calibration assessment, limit confidence in generalizability. The underrepresentation of rural populations and the scarcity of implementation studies constrain real-world applicability. Future development should prioritise federated learning for privacy-preserving multi-site collaboration, lightweight architectures for offline deployment, explainable AI frameworks, and integration with community health worker workflows to achieve equitable, scalable solutions to reduce preventable maternal deaths in rural low- and middle-income country settings.Systematic review registration  PROSPERO CRD42025174343.
SN  - 1472-6947
ER  -