A team from the University of Johannesburg uses panel data and econometric models to demonstrate that AI-driven robotics and diagnostics significantly reduce maternal mortality, with the most pronounced benefits in resource-limited settings.

Key points

• Panel DiD analysis finds post-2000 AI adoption cuts maternal mortality by over 88 deaths per 100,000 live births, especially in developing nations.
• Panel ARDL shows a long-run cointegrated relationship between AI robotics flow and maternal mortality, with developing countries correcting 27% of deviations annually.
• Forecasting with fixed-effects models predicts AI flow could lower global MMR below 20 per 100,000 by 2035, outpacing the impact of AI stock.

Why it matters: This study reveals AI’s transformative potential to bridge global healthcare gaps and accelerate maternal mortality reduction toward SDG 3.1 goals.

Researchers at Goethe University Frankfurt conducted a bibliometric study of 29,192 AI-in-medicine papers from 1969 to 2022, using the NewQIS platform and density-equalizing map procedures to chart global publication trends, socio-economic correlations, and equity patterns across countries.

Key points

• Analyzed 29,192 AI-in-medicine articles from Web of Science (1969–2022) using NewQIS bibliometric methodologies.
• Applied density-equalizing cartogram projections to visualize country-level research output and citation patterns.
• Performed Spearman correlations and regression residual analysis with GDP, GII, and AI readiness indices to assess global equity and disparities.

Why it matters: Mapping the global AI-in-medicine landscape exposes economic and innovation-driven inequities, guiding policies to foster inclusive research and deployment in underserved regions.