Abstract: Maternal and infant mortality remain critical public health challenges in Haut-Katanga, particularly during epidemic periods that strain limited healthcare infrastructure. This study evaluates the impact of Artificial Intelligence (AI) on reducing maternal and infant mortality through a retrospective analysis using generated data from 2015 to 2023. During this period, AI adoption increased from 2% to 25%, accompanied by a decline in maternal mortality from 940 to 840 deaths per 100,000 live births, and infant mortality from 85 to 62 deaths per 1,000 live births. Linear regression analysis indicates that a 1% increase in AI adoption is associated with a reduction of approximately 1.2 maternal deaths per 100,000 and 0.15 infant deaths per 1,000, respectively. Pearson correlation analysis reveals a strong negative relationship between AI adoption and both maternal (r ≈ -0.96) and infant mortality (r ≈ -0.96), and a strong positive correlation between maternal and infant mortality (r ≈ +0.98). Additionally, trends in infectious diseases show notable declines in malaria (r = -0.84) and HIV/AIDS (r = -1.00), while measles (r = +0.83), cholera (r = +0.98), and COVID-19 (r = +0.88) increased over time. AI-based interventions, particularly in epidemic prediction and diagnostics, have contributed to measurable health gains. However, implementation remains constrained by infrastructural deficiencies, limited funding, and low digital health capacity. The findings underscore AI's emerging role in improving health outcomes and emphasize the need for strategic investments in infrastructure, workforce training, and supportive policy frameworks to enhance healthcare delivery and epidemic preparedness in resource-limited settings.Abstract: Maternal and infant mortality remain critical public health challenges in Haut-Katanga, particularly during epidemic periods that strain limited healthcare infrastructure. This study evaluates the impact of Artificial Intelligence (AI) on reducing maternal and infant mortality through a retrospective analysis using generated data from 2015 to 2023....Show More
