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Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward.

Elizabeth Campbell1, Oluremilekun Oyefolu1, Sarah Gillani1

  • 1Johns Hopkins Center for Outbreak Response Innovation, 700 East Pratt Street, Suite 900, Baltimore, MD, 21202, United States, 1 6097524672.

JMIR Public Health and Surveillance
|April 27, 2026
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Summary
This summary is machine-generated.

Artificial intelligence (AI) can improve governmental decision-making during public health emergencies. Responsible AI implementation requires addressing technical, ethical, and policy challenges for equitable outcomes.

Keywords:
AIartificial intelligencedecision makingelected officialsgovernancepublic healthpublic health emergency response

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Area of Science:

  • Public Health
  • Health Informatics
  • Artificial Intelligence

Background:

  • Public health emergencies necessitate rapid decisions by officials lacking public health expertise.
  • Artificial intelligence (AI) offers potential to improve governmental crisis response.
  • Existing challenges include technical, logistical, ethical, and policy considerations.

Purpose of the Study:

  • To examine AI's potential as a decision-support tool for elected officials during public health emergencies.
  • To identify key challenges and propose solutions for AI implementation in governmental crisis response.
  • To introduce a novel AI Decision Support Lifecycle framework for public health emergency response.

Main Methods:

  • Literature review on digital health technologies and AI governance.
  • Development of the AI Decision Support Lifecycle framework.
  • Analysis of technical, ethical, and policy challenges.
  • Stakeholder-specific recommendations and case examples.

Main Results:

  • AI can enhance decision quality, transparency, and timeliness in public health crises.
  • Key challenges include model accuracy, data representativeness, privacy, fairness, transparency, and accountability.
  • Successful AI integration requires investments in capacity, infrastructure, training, engagement, and evaluation.
  • A six-phase AI Decision Support Lifecycle framework is proposed.

Conclusions:

  • AI can significantly strengthen public health decision-making when implemented responsibly.
  • Ethical AI frameworks, robust governance, and public engagement are crucial for trust and equity.
  • Addressing disparities and ensuring accountability are paramount for effective AI deployment in emergencies.