Social bias in artificial intelligence algorithms designed to improve cardiovascular risk assessment relative to the Framingham Risk Score: a protocol for a systematic review.

ABSTRACT:

Introduction

Cardiovascular disease (CVD) prevention relies on timely identification of and intervention for individuals at risk. Risk assessment models such as the Framingham Risk Score (FRS) have been shown to over-estimate or under-estimate risk in certain groups, such as socioeconomically disadvantaged populations. Artificial intelligence (AI) and machine learning (ML) could be used to address such equity gaps to improve risk assessment; however, critical appraisal is warranted before ML-informed clinical decision-making is implemented.

Methods and analysis

This study will employ an equity-lens to identify sources of bias (ie, race/ethnicity, gender and social stratum) in ML algorithms designed to improve CVD risk assessment relative to the FRS. A comprehensive literatur

SUBMITTER: Garcha I 

PROVIDER: S-EPMC10254950 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature