Abstract
This study examines the impact of Artificial Intelligence (AI) on maternal mortality in alignment with Sustainable Development Goal (SDG) 3.1, which aims to reduce maternal mortality to below 70 per 100,000 live births by 2030. Despite advancements, maternal mortality remains disproportionately high in developing countries due to weaker healthcare infrastructure.
Using panel data from 70 countries (1990-2022), sourced from WHO's Global Burden of Disease (GBD), World Bank's World Development Indicators (WDI), UNCTAD, and the World Robotics database, we apply the Difference-in-Differences (DiD) approach to assess AI's impact over time and the Auto-Regressive Distributed Lag (ARDL) model to examine short- and long-term effects.
AI adoption significantly reduces maternal mortality, particularly in developing countries, where post-2000 advancements have led to notable declines. ARDL results show that 27% of deviations from long-term maternal mortality trends are corrected annually, highlighting AI's sustained impact. The DiD analysis indicates AI's greatest benefits in resource-limited settings, including improving early diagnostics, personalized care, and remote monitoring. In developed countries, AI's effects are marginal due to existing advanced healthcare systems.
AI presents a transformative solution for reducing maternal mortality, particularly in low-resource settings. Policymakers should prioritize AI-driven healthcare, expand digital infrastructure, and ensure equitable access to maximize its benefits. AI integration is crucial for addressing maternal health disparities and accelerating progress toward SDG 3.1.