Work

Artificial intelligence in cardiovascular medicine: systematic review of randomized controlled trials

Year: 2026

Type: review

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

Source: European Heart Journal - Digital Health

DOI: 10.1093/ehjdh/ztag164