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Algorithmic fairness in computational medicine.

Jie Xu1, Yunyu Xiao2, Wendy Hui Wang3

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.

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Summary
This summary is machine-generated.

Algorithmic bias in machine learning can harm vulnerable groups in healthcare. This review explores bias types, fairness metrics, and mitigation strategies for computational medicine.

Keywords:
Algorithmic fairnessComputational medicine

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Area of Science:

  • Computational medicine
  • Health informatics
  • Machine learning in healthcare

Background:

  • Machine learning models are vital for clinical decisions but can exhibit algorithmic bias.
  • This bias disproportionately affects vulnerable populations, including ethnic minorities, impacting health outcomes.
  • Algorithmic bias in medicine is a growing concern with ongoing research into its effects and solutions.

Purpose of the Study:

  • To provide a comprehensive review of algorithmic fairness in computational medicine.
  • To detail various types of algorithmic bias encountered in medical applications.
  • To summarize metrics for quantifying fairness and methods for mitigating bias.

Main Methods:

  • Literature review of algorithmic bias in computational medicine.
  • Overview of fairness quantification metrics.
  • Summary of bias mitigation techniques and tools.

Main Results:

  • Identified different types of algorithmic bias relevant to healthcare.
  • Cataloged key metrics for assessing fairness in medical AI.
  • Summarized common strategies and software for bias mitigation.

Conclusions:

  • Addressing algorithmic bias is crucial for equitable healthcare AI.
  • Researchers and practitioners need insights into bias evaluation and mitigation.
  • This review offers a foundational reference for developing fairer computational medicine tools.