Maternal and child healthcare remains a key global health challenge in low-resource settings. Insufficient training of healthcare workers and the unavailability of skilled medical personnel have led to high maternal and neonatal mortality and pregnancy-related complications. Addressing these challenges is critical for improving healthcare outcomes in these regions. Artificial intelligence (AI) applications have demonstrated significant potential for improving maternal and child health by enabling personalized care and supporting data-driven clinical decision-making. These technologies offer practical solutions for overcoming systemic barriers. The integration of AI into health systems enables predictive analytics, telemedicine, and automated diagnostics. Projects such as interoperable AI language modules and solar-powered mobile health units have effectively enhanced maternal and child health services. Countries including the United States, India, Kenya, Uganda, and the Philippines have implemented AI models to deliver focused care for pregnant women and neonates. However, challenges such as poor infrastructure, limited acceptability, lack of medical expertise, and data confidentiality concerns remain. Enhancing healthcare professionals’ training, conducting trials of AI models, and embedding AI into existing healthcare systems can substantially reduce these limitations. With ongoing development, AI is poised to shape the future of maternal and child healthcare and address most implementation barriers. This review includes studies published between January 2010 and November 2024, with the final database search conducted on November 20, 2024.
Umar et al. (Tue,) studied this question.