Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.
Artificial intelligence
Clinical decision support systems
Deep learning
Machine learning
Neural networks
Journal
Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056
Informations de publication
Date de publication:
12 Aug 2024
12 Aug 2024
Historique:
received:
04
05
2024
accepted:
31
07
2024
medline:
12
8
2024
pubmed:
12
8
2024
entrez:
12
8
2024
Statut:
epublish
Résumé
This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screening, full-text articles were assessed for methodological quality and adherence to inclusion criteria. Data extraction focused on study design, AI technologies employed, reported outcomes, and evidence of AI-CDSS impact on patient and clinical outcomes. Thematic analysis was conducted to synthesise findings and identify key themes regarding the effectiveness of AI-CDSS. The screening of the articles resulted in the selection of 26 articles that satisfied the inclusion criteria. The content analysis revealed four themes: early detection and disease diagnosis, enhanced decision-making, medication errors, and clinicians' perspectives. AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations. Using AI in CDSSs can potentially improve patient outcomes by enhancing diagnostic accuracy, optimising treatment selection, and reducing medical errors.
Identifiants
pubmed: 39133332
doi: 10.1007/s10916-024-02098-4
pii: 10.1007/s10916-024-02098-4
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
74Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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