AUTHOR=Ige Olusimbo , Fakuade Dolapo 
  
TITLE=Executive decision support for managing AI risk in public health
  
JOURNAL=International Journal of Public Health
  
VOLUME=Volume 71 - 2026
  
YEAR=2026
  
URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2026.1610326
  
DOI=10.3389/ijph.2026.1610326
  
ISSN=1661-8564
  
ABSTRACT=Artificial Intelligence (AI) is transforming public health decision-making, shifting it from descriptive epidemiological reporting to predictive, accelerated operational guidance. Yet AI in public health is discussed almost entirely at technical or regulatory levels, leaving a gap in executive-level guidance. Public health operates within a complex landscape of social determinants, political scrutiny, and incomplete information, where algorithmic errors can severely damage public trust. Consequently, executive competency must expand beyond traditional data interpretation to foundational AI literacy. Leaders must differentiate among diverse AI applications and evaluate their risk profiles, including balancing AI trained on individual-level precision medicine models with public health population-level prevention mandate. Continuous human oversight and multidisciplinary validation should be standard for modern public health organizations. Automated systems must equitably advance population health outcomes under final human authority at all times. Public health executives can better prepare to make informed decisions about AI-related risks using the decision points presented in this commentary.