Algorithmic fairness in computational medicine. Review uri icon

Overview

abstract

  • Machine learning models are increasingly adopted for facilitating clinical decision-making. However, recent research has shown that machine learning techniques may result in potential biases when making decisions for people in different subgroups, which can lead to detrimental effects on the health and well-being of specific demographic groups such as vulnerable ethnic minorities. This problem, termed algorithmic bias, has been extensively studied in theoretical machine learning recently. However, the impact of algorithmic bias on medicine and methods to mitigate this bias remain topics of active discussion. This paper presents a comprehensive review of algorithmic fairness in the context of computational medicine, which aims at improving medicine with computational approaches. Specifically, we overview the different types of algorithmic bias, fairness quantification metrics, and bias mitigation methods, and summarize popular software libraries and tools for bias evaluation and mitigation, with the goal of providing reference and insights to researchers and practitioners in computational medicine.

publication date

  • September 6, 2022

Research

keywords

  • Clinical Decision-Making
  • Machine Learning

Identity

PubMed Central ID

  • PMC9463525

Scopus Document Identifier

  • 85137170714

Digital Object Identifier (DOI)

  • 10.1016/j.ebiom.2022.104250

PubMed ID

  • 36084616

Additional Document Info

volume

  • 84