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

©Jonathan Huang, Galal Galal, Mozziyar Etemadi, Mahesh Vaidyanathan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 31.05.2022..

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 databases, as well as Google Scholar, identified 635 records, of which 12 studies were included.

RESULTS: Applications of ML were varied and involved diagnosis, outcome prediction, and clinical score prediction performed on data sets including images, diagnostic studies, clinical text, and clinical variables. Of the 12 studies, 1 (8%) described a model in routine clinical use, 2 (17%) examined prospectively validated clinical models, and the remaining 9 (75%) described internally validated models. In addition, 8 (67%) studies concluded that racial bias was present, 2 (17%) concluded that it was not, and 2 (17%) assessed the implementation of bias mitigation strategies without comparison to a baseline model. Fairness metrics used to assess algorithmic racial bias were inconsistent. The most commonly observed metrics were equal opportunity difference (5/12, 42%), accuracy (4/12, 25%), and disparate impact (2/12, 17%). All 8 (67%) studies that implemented methods for mitigation of racial bias successfully increased fairness, as measured by the authors' chosen metrics. Preprocessing methods of bias mitigation were most commonly used across all studies that implemented them.

CONCLUSIONS: The broad scope of medical ML applications and potential patient harms demand an increased emphasis on evaluation and mitigation of racial bias in clinical ML. However, the adoption of algorithmic fairness principles in medicine remains inconsistent and is limited by poor data availability and ML model reporting. We recommend that researchers and journal editors emphasize standardized reporting and data availability in medical ML studies to improve transparency and facilitate evaluation for racial bias.

Medienart:	E-Artikel

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	Zur Gesamtaufnahme - volume:10
Enthalten in:	JMIR medical informatics - 10(2022), 5 vom: 31. Mai, Seite e36388

Sprache:	Englisch

Beteiligte Personen:	Huang, Jonathan [VerfasserIn] Galal, Galal [VerfasserIn] Etemadi, Mozziyar [VerfasserIn] Vaidyanathan, Mahesh [VerfasserIn]

Links:	Volltext

Themen:	Algorithm Algorithmic fairness Artificial intelligence Assessment Bias Clinical machine learning Diagnosis Fairness Journal Article Machine learning Medical machine learning Mitigation Model Outcome prediction Prediction Race Racial bias Review Scoping review Score prediction

Anmerkungen:	Date Revised 31.07.2022 published: Electronic Citation Status PubMed-not-MEDLINE

doi:	10.2196/36388

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM341616397