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  3. An Exploration Of Machine Learning Methods In Human Biomonitoring.
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  3. An Exploration Of Machine Learning Methods In Human Biomonitoring.

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An Exploration of Machine Learning Methods in Human Biomonitoring.

Kavita Singh1, Jiazhou Bi1, Malo Musende1,2

  • 1Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada.

International Journal of Environmental Research and Public Health
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI), specifically machine learning (ML), is increasingly used in human biomonitoring for data analysis. A key barrier to AI adoption in biomonitoring is the lack of technical expertise among researchers.

Keywords:
artificial intelligencebiomonitoringmachine learning

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Area of Science:

  • Environmental Health Sciences
  • Computational Biology
  • Toxicology

Background:

  • Artificial intelligence (AI) and machine learning (ML) offer advanced capabilities for managing and analyzing large datasets.
  • Human biomonitoring collects crucial data on chemical exposure and health outcomes.
  • Integrating AI/ML into biomonitoring can enhance data interpretation and predictive power.

Purpose of the Study:

  • To explore the implementation of AI/ML methods in human biomonitoring.
  • To review current practices, applications, and researcher perceptions of AI in biomonitoring.
  • To identify barriers hindering the adoption of AI in the field.

Main Methods:

  • A mixed-methods approach combining a scoping literature review and an international survey of biomonitoring programs.
  • The literature review identified and categorized 286 studies applying ML to human biomonitoring data.
  • An online survey gathered data on AI implementation, perspectives, and barriers from 30 biomonitoring programs across 15 countries.

    • Main Results:

    • The review identified 82 ML methods, with supervised approaches being most common, predominantly used for predicting health outcomes from chemical exposure.
    • Approximately 27% of surveyed biomonitoring programs reported using AI-related methods.
    • A significant barrier (80%) to AI adoption was identified as a lack of technical expertise.

    Conclusions:

    • Machine learning shows significant promise for advancing the understanding of chemical exposure in human populations.
    • Continued growth in AI applications within human biomonitoring is anticipated.
    • Addressing the technical expertise gap is crucial for broader AI adoption in biomonitoring.