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  3. Machine Learning Applications In Population And Public Health: Guidelines For Development, Testing, And Implementation.
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  3. Machine Learning Applications In Population And Public Health: Guidelines For Development, Testing, And Implementation.

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Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and

Andrew D Pinto1,2,3,4, Sharon Birdi1, Steve Durant1

  • 1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada, 1 4168646060 ext 76148.

JMIR Public Health and Surveillance
|October 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning (ML) offers powerful tools for public health, but can produce biased results. New guidelines address ethical ML use, focusing on disadvantaged communities, transparency, and risk assessment for equitable health outcomes.

Keywords:
AIalgorithmic biasartificial intelligenceguidelinehealth equitymachine learningpopulation healthpublic health

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Area of Science:

  • Public Health
  • Artificial Intelligence
  • Machine Learning

Background:

  • Machine learning (ML), a subset of artificial intelligence, is increasingly utilized in population and public health for tasks like disease outbreak prediction and intervention assessment.
  • Despite its utility, ML can generate biased outputs due to data quality, analytical direction, and interpretation issues.
  • Currently, specific guidelines for the ethical application of ML in public health are lacking.

Purpose of the Study:

  • To develop evidence-based guidelines for the ethical and effective use of machine learning in population and public health.
  • To address potential biases and ensure equitable application of ML tools in diverse health contexts.

Main Methods:

  • A multidisciplinary expert team was assembled, including specialists in computer science, epidemiology, ethics, and public health.
  • Literature reviews and a modified Delphi process were employed to identify key recommendations.
  • The process focused on practical, operational steps for stakeholders.

    • Main Results:

    • Five key recommendations were identified: prioritizing disadvantaged communities, ethical use in dynamic situations, conducting risk and bias assessments, ensuring technical transparency and reproducibility, and fostering multidisciplinary dialogue.
    • These recommendations aim to mitigate ML-related bias and promote awareness.
    • The guidelines provide operational steps for stakeholders to ensure ML tools are effective, ethical, and feasible.

    Conclusions:

    • The developed guidelines offer a framework for responsible ML implementation in public health.
    • Adherence to these recommendations can help ensure ML tools are used equitably and effectively.
    • Fostering dialogue and transparency is crucial for mitigating potential harms of ML bias in public health.