Artificial intelligence in cardiovascular medicine: systematic review of randomized controlled trials
Abstract
Aims Artificial intelligence (AI) holds considerable promise for improving cardiovascular care, but prior to widespread adoption, AI-powered tools require evaluation in randomized trials. We systematically reviewed the current evidence from randomized trials of AI tools in cardiovascular medicine. Methods and results We conducted a systematic review of randomized trials comparing AI tools vs. standard care in cardiovascular medicine. PubMed, the Cochrane Central Register of Controlled Trials, Scopus, and the International Clinical Trials Registry Platform were searched from 1 January 2018, to 10 April 2026. Screening and full-text review were performed by three independent reviewers and risk of bias was assessed using the RoB 2 criteria. Of 11 151 identified records, 8599 remained after removal of duplicates. Following screening and full-text review, 25 trials met the eligibility criteria. These trials evaluated AI tools in acute cardiovascular care (n = 3), cardiac imaging (n = 6), decision support for medical therapy (n = 6), electrophysiology (n = 8), and interventional cardiology (n = 2). Nine trials were single-centre, and two were international. Seventeen trials (68%) reported positive results, defined by non-inferiority or superiority of the AI-based strategy. Overall risk of bias was rated as high or with some concerns in 84% of the included trials, and only 36% explicitly reported following the CONSORT-/SPIRIT-AI reporting guidelines. Conclusion Evidence from randomized trials evaluating AI tools in cardiovascular medicine is limited, and most trials have a high risk of bias. Wider implementation of AI tools into practice requires robust, international, multi-centre randomized trials that evaluate patient-relevant outcomes, ensure external validity, and adhere to reporting guidelines. Trial registration number PROSPERO CRD420251019264
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BibTeX
@article{kunz2026artificial,
title = {{Artificial intelligence in cardiovascular medicine: systematic review of randomized controlled trials}},
author = {Michael Kunz and Tiffany Péquignot and Tobias Zimmermann and Ivo Strebel and Wayne Zeng and Sven Knecht and Julia Freiermuth and Arnaud Tanguy Champetier and Koray Durak and Dimitrios Doudesis and Kuan Ken Lee and Andrew R Chapman and Gregor Leibundgut and Felix Mahfoud and Gary S Collins and Richard D Riley and Nicholas L Mills and Christian Mueller and Pedro Lopez Ayala and Jasper Boeddinghaus},
journal = {European Heart Journal. Digital Health},
year = {2026},
month = oct,
doi = {10.1093/ehjdh/ztag164},
url = {https://doi.org/10.1093/ehjdh/ztag164},
note = {Public domain},
}
RIS
TY - JOUR
TI - Artificial intelligence in cardiovascular medicine: systematic review of randomized controlled trials
AU - Michael Kunz
AU - Tiffany Péquignot
AU - Tobias Zimmermann
AU - Ivo Strebel
AU - Wayne Zeng
AU - Sven Knecht
AU - Julia Freiermuth
AU - Arnaud Tanguy Champetier
AU - Koray Durak
AU - Dimitrios Doudesis
AU - Kuan Ken Lee
AU - Andrew R Chapman
AU - Gregor Leibundgut
AU - Felix Mahfoud
AU - Gary S Collins
AU - Richard D Riley
AU - Nicholas L Mills
AU - Christian Mueller
AU - Pedro Lopez Ayala
AU - Jasper Boeddinghaus
PY - 2026
DA - 2026/10/12
JO - European Heart Journal. Digital Health
DO - 10.1093/ehjdh/ztag164
UR - https://doi.org/10.1093/ehjdh/ztag164
AB - Aims Artificial intelligence (AI) holds considerable promise for improving cardiovascular care, but prior to widespread adoption, AI-powered tools require evaluation in randomized trials. We systematically reviewed the current evidence from randomized trials of AI tools in cardiovascular medicine. Methods and results We conducted a systematic review of randomized trials comparing AI tools vs. standard care in cardiovascular medicine. PubMed, the Cochrane Central Register of Controlled Trials, Scopus, and the International Clinical Trials Registry Platform were searched from 1 January 2018, to 10 April 2026. Screening and full-text review were performed by three independent reviewers and risk of bias was assessed using the RoB 2 criteria. Of 11 151 identified records, 8599 remained after removal of duplicates. Following screening and full-text review, 25 trials met the eligibility criteria. These trials evaluated AI tools in acute cardiovascular care (n = 3), cardiac imaging (n = 6), decision support for medical therapy (n = 6), electrophysiology (n = 8), and interventional cardiology (n = 2). Nine trials were single-centre, and two were international. Seventeen trials (68%) reported positive results, defined by non-inferiority or superiority of the AI-based strategy. Overall risk of bias was rated as high or with some concerns in 84% of the included trials, and only 36% explicitly reported following the CONSORT-/SPIRIT-AI reporting guidelines. Conclusion Evidence from randomized trials evaluating AI tools in cardiovascular medicine is limited, and most trials have a high risk of bias. Wider implementation of AI tools into practice requires robust, international, multi-centre randomized trials that evaluate patient-relevant outcomes, ensure external validity, and adhere to reporting guidelines. Trial registration number PROSPERO CRD420251019264
SN - 2634-3916
ER -