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<p>This study examines the effect of Artificial Intelligence (AI) on infant mortality rates (IMR) across 102 countries from 2000 to 2023, considering the moderating roles of governance quality (GQI), digital infrastructure (DII), and medical infrastructure (MII). Grounded in the Health Production Function, Institutional, Technological Systems and Absorptive Capacity theories, the study explores how institutional, technological and healthcare readiness influence AI’s effectiveness in improving healthcare outcomes. Multiple estimation techniques, such as Pooled Ordinary Least Squares (POLS), Fully Modified Ordinary Least Squares (FMOLS), Driscoll–Kraay Standard Errors (DKSE), Two-Stage Least Squares (2SLS), and Method of Moments Quantile Regression (MMQR), were employed to address heterogeneity and endogeneity concerns. Results across all estimators are consistent and show that AI significantly reduces IMR by enhancing diagnostic precision, healthcare resource allocation, and data-driven decision-making. The moderating effects of GQI, DII, and MII are negative and significant, suggesting that strong governance, robust digital networks, and advanced medical systems amplify the effectiveness of AI. Quantile regression results indicate heterogeneous effects, with stronger impacts in technologically advanced and low-mortality contexts, and weaker impacts where structural constraints exist. Subsample analysis reveals that AI reduces IMR in both developing and developed economies, but the effect is greater in the latter due to institutional maturity. Overall, the study highlights that AI’s transformative potential in reducing infant mortality depends critically on governance capacity, digital readiness, and healthcare system strength.</p>

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How does artificial intelligence influence infant mortality?: a cross-country analysis (2000–2023)

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Partha Acharjee,
  • Debasis Neogi,
  • Sauvik Chakraborty

摘要

This study examines the effect of Artificial Intelligence (AI) on infant mortality rates (IMR) across 102 countries from 2000 to 2023, considering the moderating roles of governance quality (GQI), digital infrastructure (DII), and medical infrastructure (MII). Grounded in the Health Production Function, Institutional, Technological Systems and Absorptive Capacity theories, the study explores how institutional, technological and healthcare readiness influence AI’s effectiveness in improving healthcare outcomes. Multiple estimation techniques, such as Pooled Ordinary Least Squares (POLS), Fully Modified Ordinary Least Squares (FMOLS), Driscoll–Kraay Standard Errors (DKSE), Two-Stage Least Squares (2SLS), and Method of Moments Quantile Regression (MMQR), were employed to address heterogeneity and endogeneity concerns. Results across all estimators are consistent and show that AI significantly reduces IMR by enhancing diagnostic precision, healthcare resource allocation, and data-driven decision-making. The moderating effects of GQI, DII, and MII are negative and significant, suggesting that strong governance, robust digital networks, and advanced medical systems amplify the effectiveness of AI. Quantile regression results indicate heterogeneous effects, with stronger impacts in technologically advanced and low-mortality contexts, and weaker impacts where structural constraints exist. Subsample analysis reveals that AI reduces IMR in both developing and developed economies, but the effect is greater in the latter due to institutional maturity. Overall, the study highlights that AI’s transformative potential in reducing infant mortality depends critically on governance capacity, digital readiness, and healthcare system strength.