Abstract
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.
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We express our gratitude for the administrative support provided by Miss Ming-Hsuan Chang from the AI Impact Research Center at Taipei Veterans General Hospital. Funding sources include grants from Taipei Veterans General Hospital (VN115-11, V115E-004-1) and the Ministry of Health and Welfare, Taiwan (MOHW113-IM-I-212-000013-16, MOHW114-IM-1-212-000004-5, MOHW114-58070-03-1-2). The funding source had no role in the design, data collection, analysis, interpretation, or writing of the manuscript.
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S.-J.W. reports grants/contracts from Eli Lilly Taiwan, Novartis, and Orient Europharma; consulting fees from AbbVie, Eli Lilly Taiwan, Percept Co., and Pfizer; and honoraria from AbbVie, Biogen, Eli Lilly, Hava Biopharma, and Pfizer. All other authors declare no competing financial or non-financial interests.
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Lin, YE., Yang, SM., Huang, CJ. et al. Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02690-7
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DOI: https://doi.org/10.1038/s41746-026-02690-7
