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Area of Science:

  • Biomedical Data Science
  • Environmental Sustainability
  • Computational Ethics

Background:

  • Biomedical data science offers significant potential for advancing human health.
  • The computational infrastructure required is energy- and resource-intensive, posing environmental challenges.
  • Current efforts to mitigate these harms are in early stages.

Purpose of the Study:

  • To comprehensively review the environmental harms of biomedical data science.
  • To explore technical, ethical, sociological, and regulatory approaches to mitigation.
  • To propose strategies for accelerating the institutionalization of sustainable practices.

Main Methods:

  • Literature review of existing efforts in environmental mitigation within biomedical data science.
  • Analysis of historical institutionalization of animal research ethics for lessons learned.
  • Identification of persistent challenges and future considerations.

Main Results:

  • A growing consensus exists on the need to address the environmental impact.
  • Progress in institutionalizing mitigation strategies is slow.
  • Key lessons from animal research ethics can inform current efforts.

Conclusions:

  • Accelerating the adoption of sustainable practices requires learning from historical precedents.
  • Addressing challenges like the rebound paradox and global equity is crucial.
  • Ensuring a responsible and sustainable future for biomedical data science is paramount.