The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions

Abstract

This debate paper examines the transformative potential of Artificial Intelligence (AI), specifically through Machine Learning (ML), in enhancing preventive measures in maternal and child health (MCH). With the proliferation of Big Data, ML has become crucial in handling complex, non-linear interactions among health determinants to not only predict but also prevent adverse outcomes. This paper underscores AI's applications in early interventions that could decrease the incidence of MCH issues. It reviews technological advancements while addressing ethical, practical, and data-related challenges in applying AI in preventive healthcare. Emphasis is placed on recent supervised, unsupervised, and reinforcement learning applications that significantly advance preventive care, particularly in low-resource settings. The manuscript discusses the development of AI models for early diagnosis, comprehensive risk assessments, and customized preventive interventions, while highlighting challenges like data diversity, privacy issues, and integrating multimodal health data.

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