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<p>Artificial intelligence (AI) is progressively utilized in cardiology; nonetheless, the overarching advantages across various care domains remain ambiguous. We conducted a search of PubMed, Embase, CINAHL, and trial registries for randomized controlled trials up to January 16, 2026, assessing prospectively applied interventions based on machine/deep-learning algorithms while excluding rule-based systems. Endpoints were categorized according to NICE evidence tiers: workflow efficiency (Tier A), patient engagement/health promotion (Tier B), and clinical outcomes (Tier C). The risk of bias was evaluated using RoB 2.0. In 32 randomized controlled trials (27 of which were meta-analyzed), artificial intelligence improved all levels. Tier A: workflow time reduced (SMD − 0.71; 95% CI − 1.04 to −0.39), corresponding to a diagnostic time that is 30–120 s shorter and a decrease of 1.0–4.2 hospital days in trials reporting length of stay. Tier B: Behavioral nudging enhanced medication adherence (RR 1.59; 95% CI 1.01–2.50; NNT = 12). Tier C: decision-support implementations decreased all-cause mortality (RR 0.84; 95% CI 0.75–0.94; <i>I</i>² = 8%; NNT = 32). Limitations encompassed restricted blinding and insufficient sham-AI controls. Data-driven clinical AI yields quantifiable efficiency improvements, enhances engagement, and reduces adverse outcomes when integrated with actionable decision support, hence informing a structured framework for governance and implementation.</p>

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Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月6日

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  • Yi-En Lin,
  • Shu-Mei Yang,
  • Chi-Jung Huang,
  • Yi-Wen Tsai,
  • Hao-Min Cheng,
  • Wui-Chiang Lee,
  • Shuu-Jiun Wang

摘要

Artificial intelligence (AI) is progressively utilized in cardiology; nonetheless, the overarching advantages across various care domains remain ambiguous. We conducted a search of PubMed, Embase, CINAHL, and trial registries for randomized controlled trials up to January 16, 2026, assessing prospectively applied interventions based on machine/deep-learning algorithms while excluding rule-based systems. Endpoints were categorized according to NICE evidence tiers: workflow efficiency (Tier A), patient engagement/health promotion (Tier B), and clinical outcomes (Tier C). The risk of bias was evaluated using RoB 2.0. In 32 randomized controlled trials (27 of which were meta-analyzed), artificial intelligence improved all levels. Tier A: workflow time reduced (SMD − 0.71; 95% CI − 1.04 to −0.39), corresponding to a diagnostic time that is 30–120 s shorter and a decrease of 1.0–4.2 hospital days in trials reporting length of stay. Tier B: Behavioral nudging enhanced medication adherence (RR 1.59; 95% CI 1.01–2.50; NNT = 12). Tier C: decision-support implementations decreased all-cause mortality (RR 0.84; 95% CI 0.75–0.94; I² = 8%; NNT = 32). Limitations encompassed restricted blinding and insufficient sham-AI controls. Data-driven clinical AI yields quantifiable efficiency improvements, enhances engagement, and reduces adverse outcomes when integrated with actionable decision support, hence informing a structured framework for governance and implementation.