Executive decision support for managing AI risk in public health

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.

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BibTeX RIS

BibTeX

@article{ige2026executive,
  title = {{Executive decision support for managing AI risk in public health}},
  author = {Olusimbo Ige and Dolapo Fakuade and Dolapo Fakuade},
  journal = {International Journal of Public Health},
  year = {2026},
  month = oct,
  doi = {10.3389/ijph.2026.1610326},
  url = {https://doi.org/10.3389/ijph.2026.1610326},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - Executive decision support for managing AI risk in public health
AU  - Olusimbo Ige
AU  - Dolapo Fakuade
AU  - Dolapo Fakuade
PY  - 2026
DA  - 2026/10/26
JO  - International Journal of Public Health
DO  - 10.3389/ijph.2026.1610326
UR  - https://doi.org/10.3389/ijph.2026.1610326
AB  - 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.
SN  - 1661-8564
SN  - 1661-8556
ER  -

Abstract

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