Algorithmic fairness in computational medicine

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 peo-ple in different subgroups, which can lead to detrimental effects on the health and well-being of specific demo-graphic 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.Copyright (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Automated summary

Machine learning models are increasingly being used in clinical decision-making, but recent research has highlighted the potential biases that these techniques may introduce, particularly for vulnerable ethnic minorities. This paper provides a comprehensive review of algorithmic fairness in computational medicine, discussing different types of bias, metrics for quantifying fairness, and methods for mitigating bias. It also summarizes popular software libraries and tools for evaluating and mitigating bias, serving as a valuable resource for researchers and practitioners in computational medicine.