Evidence map›Paper›PMID 42602970›Full record

ArticleEClinicalMedicine2026

Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.

Angus Qi Chwen Ong, Chin-Siang Ang, Iva Bojic, Casey L Johnson, Hesham Aggour, Fu Siong Ng, Josip Car, Paul Leeson, Judite Gonçalves, Nai Ming Lai

Abstract read
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In one paragraph

Article in EClinicalMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Angus Qi Chwen OngSchool of Public Health, Imperial College London, London, UK.
Chin-Siang AngLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Iva BojicLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Casey L JohnsonOxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Hesham AggourNational Heart and Lung Institute, Imperial College London, London, UK.
Fu Siong NgNational Heart and Lung Institute, Imperial College London, London, UK.
Josip CarKing's Population Health Institute, King's College London, London, UK.
Paul LeesonOxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Judite GonçalvesSchool of Public Health, Imperial College London, London, UK.
Nai Ming LaiSchool of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) holds potential to transform cardiovascular care, but evidence on its effectiveness in clinical practice remains inconsistent. We aimed to synthesise evidence from randomised controlled trials (RCTs) on the effectiveness of AI-enabled cardiovascular care, summarise the trial design and characteristics of AI systems, and evaluate methodological quality and reporting transparency. Methods: In this systematic review and meta-analysis, we searched Embase, MEDLINE, Scopus, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov for RCTs that evaluated effectiveness of AI interventions in cardiovascular care, published in English from database inception to July 07, 2025. The search was updated on April 28, 2026. We followed Cochrane guidance for study selection and data extraction. Risk of bias was assessed using Cochrane's Risk of Bias tools and reporting transparency using CONSORT-AI checklist. We calculated summary effects using inverse-variance-weighted random-effects meta-analyses and assessed the certainty of evidence using GRADE. Between-study heterogeneity was quantified using χ Findings: Of 12,217 records identified, 31 RCTs from 13 regions (n = 1,685,717 patients) were included in the systematic review, and 11 of these (n = 1,614,689) in the meta-analysis. Most RCTs were published after 2021 (90%), multicentre (58%), and had short follow-up duration (<12 months; 65%). Risk of bias was low in seven trials (23%), and overall reporting transparency was moderate. Twenty-two trials (71%) reported significant benefit of AI interventions on primary endpoints, mostly intermediate process measures, while nine trials (29%) found no significant effect. Compared with routine care, image-based AI-clinical decision support system had significant effect on major adverse cardiovascular events (risk ratio [RR] 0.74 [95% CI 0.58-0.96]; I Interpretation: AI demonstrated potential benefits in intermediate process measures in cardiovascular care but evidence of its effect on hard clinical outcomes remains limited. Current evidence is constrained by high risk of bias, trial heterogeneity, inadequate reporting of AI-specific components, limited generalisability, and a scarcity of trials with clinically meaningful endpoints. More rigorous, representative, and transparently reported RCTs with longer follow-up and long-term clinical outcomes are needed. Funding: None received.

Indexed as

Artificial intelligenceCardiologyCardiovascular diseaseClinical trialEvidence synthesisMachine learning

Identifiers

PMID42602970
PMCPMC13475654

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.