Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure.

Artificial Intelligence Health Equity Heart Failure / therapy Humans
Artificial intelligence Guideline-directed therapy Health equity Health services research Machine learning Racial disparities Risk prediction

Journal

Heart failure clinics
ISSN: 1551-7136
Titre abrégé: Heart Fail Clin
Pays: United States
ID NLM: 101231934

Informations de publication

Date de publication:
Apr 2022
Historique:
entrez: 28 3 2022
pubmed: 29 3 2022
medline: 1 4 2022
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.hfc.2021.11.001 PMID: 35341539 PMC: PMC8988237
pubmed: 35341539
pii: S1551-7136(21)00110-0
doi: 10.1016/j.hfc.2021.11.001
pmc: PMC8988237
mid: NIHMS1786622
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

259-273

Subventions

Organisme : NHLBI NIH HHS
ID : L30 HL148881
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002378
Pays : United States
Organisme : NCATS NIH HHS
ID : KL2 TR002381
Pays : United States
Organisme : NHLBI NIH HHS
ID : K01 HL142848
Pays : United States
Organisme : NHLBI NIH HHS
ID : R56 HL159216
Pays : United States
Organisme : NIMHD NIH HHS
ID : R21 MD013490
Pays : United States

Informations de copyright

Copyright © 2021 Elsevier Inc. All rights reserved.

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Auteurs

Amber E Johnson (AE)

University of Pittsburgh School of Medicine, Heart and Vascular Institute, Veterans Affairs Pittsburgh Health System, 200 Lothrop Street, Pittsburgh, PA 15213, USA.

LaPrincess C Brewer (LC)

Division of Preventive Cardiology, Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA.

Melvin R Echols (MR)

Division of Cardiovascular Medicine, Morehouse School of Medicine, 720 Westview Drive, Atlanta, GA 30310, USA.

Sula Mazimba (S)

Division of Cardiovascular Medicine, Advanced Heart Failure and Transplant Center, University of Virginia, 2nd Floor, 1221 Lee Street, Charlottesville, VA 22903, USA.

Rashmee U Shah (RU)

Division of Cardiovascular Medicine, University of Utah, 30 N 1900 E, Cardiology, 4A100, Salt Lake City, UT 84132, USA.

Khadijah Breathett (K)

Division of Cardiovascular Medicine, Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, PO Box 245046, Tucson, AZ 85724, USA. Electronic address: kbreathett@shc.arizona.edu.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Utilizing Artificial Intelligence to Enhance...
questionsmedicales.fr › Qualité, accès, évaluation des soins de santé › Prestations des soins de santé › Accessibilité des services de santé › Équité en santé › Utilizing Artificial Intelligence to Enhance...
questionsmedicales.fr › Maladies cardiovasculaires › Cardiopathies › Défaillance cardiaque › Utilizing Artificial Intelligence to Enhance...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Utilizing Artificial Intelligence to Enhance...