Algorithmic fairness in computational medicine.

TitleAlgorithmic fairness in computational medicine.
Publication TypeJournal Article
Year of Publication2022
AuthorsXu J, Xiao Y, Wang WHui, Ning Y, Shenkman EA, Bian J, Wang F
Date Published2022 Oct
KeywordsBias, Clinical Decision-Making, Decision Making, Humans, Machine Learning

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.

Alternate JournalEBioMedicine
PubMed ID36084616
PubMed Central IDPMC9463525
Grant ListR21 CA245858 / CA / NCI NIH HHS / United States
RF1 AG072449 / AG / NIA NIH HHS / United States
R21 AG068717 / AG / NIA NIH HHS / United States
R01 AG076234 / AG / NIA NIH HHS / United States
R01 MH124740 / MH / NIMH NIH HHS / United States
R01 CA246418 / CA / NCI NIH HHS / United States
Institute of Artificial Intelligence for Digital Health
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