Bolarinwa, Obasanjo ORCID: https://orcid.org/0000-0002-9208-6408, Mohammed, Aliu and Igharo, Victor (2024) The role of artificial intelligence in transforming maternity services in Africa: prospects and challenges. Therapeutic Advances in Reproductive Health, 18.

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Abstract

Maternal and neonatal health outcomes in Africa remain a significant public health challenge.1 Despite ongoing efforts to enhance maternity services, many women across the continent do not receive the necessary antenatal care (ANC), and a substantial number of births and deliveries still occur outside health facilities, leading to preventable complications and fatalities.2 In the same vein, postnatal care (PNC), which is also crucial for the health and well-being of mothers and their babies, is also underutilised.2
This trajectory continues because Africa faces hurdles, such as limited healthcare infrastructure, shortages of skilled healthcare providers, and socio-cultural barriers that impede access to maternity care.3 Consequently, these challenges contribute to delayed or inadequate care, exacerbating adverse maternal and neonatal health outcomes.4 Furthermore, the high prevalence of infectious diseases complicates pregnancy and childbirth, increasing risks for both mothers and infants.5
Given these pressing issues, this commentary discusses the potential of leveraging artificial intelligence (AI) to bridge the gaps in maternity care, such as ANC, health facility deliveries and PNC across the continent. AI could offer substantial promise in addressing these gaps in maternity services delivery and utilisation, with various applications that could detect maternal health issues such as pregnancy-related complications or preterm labour.6 Similarly, AI can also predict post-delivery complications.7 By leveraging AI, there is a significant opportunity to enhance maternal and newborn health outcomes in Africa, ensuring comprehensive and timely care throughout the maternity continuum.

Item Type:	Article
Status:	Published
DOI:	10.1177/26334941241288587
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/10851

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