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Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving

Marianne W M C Six Dijkstra1,2,3, Egbert Siebrand4, Steven Dorrestijn4

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Summary
This summary is machine-generated.

Integrating machine learning (ML) decision support tools (DSTs) in occupational health raises ethical concerns. Careful design is needed to ensure patient autonomy, beneficence, non-maleficence, and justice, minimizing risks like discrimination.

Keywords:
Clinical decision support systemEthicsEvidence based practiceMachine learningMoralsOccupational health

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Area of Science:

  • Occupational Health
  • Medical Ethics
  • Health Informatics

Background:

  • Machine learning (ML) and computer algorithms are increasingly integrated into clinical decision support systems (DSTs).
  • The application of ML-DSTs in occupational health care presents novel ethical considerations and potential consequences for healthcare professionals and clients.
  • Understanding these implications is crucial for responsible implementation.

Purpose of the Study:

  • To explore the ethical considerations and potential consequences of utilizing ML-based DSTs within the occupational health care context.
  • To analyze the impact of ML-DSTs on core biomedical ethical principles.

Main Methods:

  • An ethical deliberation process was employed.
  • A narrative literature review of ML and DSTs in occupational health was conducted.
  • Potential impacts were assessed using frameworks from medical ethics and philosophy of technology, including a hypothetical clinical scenario.

Main Results:

  • Respect for autonomy is challenged by uncertainty regarding long-term consequences and ML-DST validity.
  • Beneficence is influenced as ML-DSTs affect evidence-based practice components.
  • Non-maleficence is tested by balancing group benefits against individual harm, worker vulnerability, and potential function creep.
  • Justice may be enhanced by valid ML-DSTs but risks profiling and discrimination.

Conclusions:

  • Ethical considerations for ML-DSTs in occupational health necessitate socially responsible design.
  • Three recommendations are provided to mitigate adverse effects during the development and implementation of ML-DSTs.
  • Addressing these ethical challenges is vital for the safe and effective integration of ML in occupational healthcare.