Emerging Technologies for Maternal and Child Health: A Systematic Review on the Potential for Mortality Reduction

Despite the evident achievements of every other sector, maternal and child mortality continues to be a significant public health problem in Nigeria due to preventable complications, delayed access to care, and low availability of skilled services. The goal of this systematic review is to investigate the potential of emerging technologies in reducing mortality in Nigeria, focusing on the technologies such as General Artificial Intelligence, fog computing, and federated learning. The literature identified, screened, assessed and selected relevant articles through a systematic review process, using the PRISMA guidelines 2020 protocol. 121 records initially were identified from major academic databases, 24 were removed as duplicates, 97 were screened and 54 were assessed for full-text reports and were eligible. After excluding studies based on their full text, 31 studies were included in the review. The findings show that the existing technologies AI and IoT can help to predict, monitor, diagnose, and make clinical decisions at early stages, and that mHealth and telemedicine can enhance the access to healthcare services and communication. However, Fog computing, a new technology, has a better potential to provide localized processing, latency reduction, and greater resilience in areas where connectivity will be unreliable, while federated learning allows for collaborative development of AI without the need for frequent centralization of patient data, thus embarrassing decentralization of patient data. The review thus underscores the potential of an integrated sustainable, equitable, context-sentitive maternal and child healthcare architecture to be based on the IoT–fog–federated learning–AI paradigm in Nigeria.

Maternal and Child Health, Mortality Reduction, Fog Computing, Federated Learning, General Artificial Intelligence

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