Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review.

ABSTRACT:

Background

Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thoroughly examined and best practices for bias mitigation remain unclear.

Objective

Our objective was to perform a scoping review to characterize the methods by which the racial bias of ML has been assessed and describe strategies that may be used to enhance algorithmic fairness in clinical ML.

Methods

A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews. A literature search using PubMed, Scopus, and Embase dat

SUBMITTER: Huang J 

PROVIDER: S-EPMC9198828 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature