| Literature DB >> 35341539 |
Amber E Johnson1, LaPrincess C Brewer2, Melvin R Echols3, Sula Mazimba4, Rashmee U Shah5, Khadijah Breathett6.
Abstract
Patients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes. This review highlights AI as a strategy to address racial inequalities in HF; discusses key AI definitions within a health equity context; describes the current uses of AI in HF, strengths and harms in using AI; and offers recommendations for future directions.Entities:
Keywords: Artificial intelligence; Guideline-directed therapy; Health equity; Health services research; Machine learning; Racial disparities; Risk prediction
Mesh：
Year: 2022 PMID： 35341539 PMCID： PMC8988237 DOI： 10.1016/j.hfc.2021.11.001
Source DB: PubMed Journal: Heart Fail Clin ISSN： 1551-7136 Impact factor: 3.179