Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.

ABSTRACT:

Background

The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequitable performance of algorithms across demographic groups may widen health inequalities.

Objective

Here, we identify and characterize bias in cardiology algorithms, looking specifically at algorithms used in the management of heart failure.

Methods

Stage 1 involved a literature search of PubMed and Web of Science for key terms relating to cardiac machine learning (ML) algorithms. Papers that built ML models to predict cardiac disease were evaluated for their focus on demographic bias in model performance, and open-source dat

SUBMITTER: Straw I 

PROVIDER: S-EPMC11384168 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature