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- Title
Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.
- Authors
Ouanes, Khaled; Farhah, Nesren
- Abstract
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.
- Subjects
MEDICATION error prevention; CHRONIC disease diagnosis; RISK assessment; PREDICTION models; ARTIFICIAL intelligence; CLINICAL decision support systems; MEDICAL care; CONTENT analysis; EVALUATION of medical care; DECISION making in clinical medicine; ACUTE kidney failure; THEMATIC analysis; SYSTEMATIC reviews; MEDLINE; ATTITUDES of medical personnel; DEEP learning; ARTIFICIAL neural networks; SEPSIS; COMPUTER-aided diagnosis; QUALITY assurance; EARLY diagnosis; EVIDENCE-based medicine; MACHINE learning; ONLINE information services; MEDICAL practice; MEDICAL care costs; SENSITIVITY &; specificity (Statistics); DISEASE risk factors
- Publication
Journal of Medical Systems, 2024, Vol 48, Issue 1, p1
- ISSN
0148-5598
- Publication type
Article
- DOI
10.1007/s10916-024-02098-4