Artificial intelligence in cardiovascular medicine: systematic review of randomized controlled trials
In brief
84% of cardiovascular AI trials had bias concerns
A review of 25 randomized trials found that 17 reported AI strategies were non-inferior or superior to standard care, but 84% had high risk of bias or some concerns. Only 36% explicitly followed AI-specific reporting guidelines, and most studies were small in scope; robust, multicentre trials measuring patient-relevant outcomes are still needed before wider adoption.
- Journal
- European heart journal. Digital health (Q1)
- Published
- 12 October 2026
- Study design
- Systematic review / meta-analysis of RCTs
- Evidence level
- Level 1, High (CEBM 1a)
- Authors
- Michael Kunz, Tiffany Péquignot, Tobias Zimmermann, Ivo Strebel, Wayne Zeng, Sven Knecht, et al.
- PMID
- 42985432
- DOI
- 10.1093/ehjdh/ztag164
Why clinicians should know about it
- Picked for Health Informatics (paper of the day, 6 November 2026): Systematic review evaluates AI tools and implementation-relevant clinical outcomes
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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.