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.
摘要：
这篇综述旨在评估AI驱动的CDS对患者预后和临床实践的有效性。在PubMed进行了全面搜索，MEDLINE,还有Scopus.2018年1月至2023年11月发表的研究有资格纳入。在标题和摘要筛选之后，对全文的方法学质量和纳入标准的依从性进行了评估.数据提取侧重于研究设计，采用的AI技术，报告的结果，以及AI-CDSS对患者和临床结局影响的证据。进行了主题分析，以综合发现并确定有关AI-CDSS有效性的关键主题。对条款的筛选导致选择了26条符合纳入标准的条款。内容分析揭示了四个主题：早期发现和疾病诊断，加强决策，用药错误,和临床医生的观点。发现基于AI的CDS通过提供患者特异性信息和基于证据的建议来改善临床决策。在CDS中使用AI可以通过提高诊断准确性来改善患者的预后，优化治疗选择，减少医疗错误。
