ArticleEuropean heart journal. Digital health2022
Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Telemedicine and artificial intelligence in family medicine practice: a systematic review and meta-analysis of barriers and enablers in routine primary care.Frontiers in digital health · 2026Pooled it
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.Nature communications · 2024Pooled it
- Systematic, randomized atrial fibrillation screening using detailed phenotyping with a risk prediction model combined with patch electrocardiogram in a Swedish population aged 65 years or older: the CONSIDERING-AF trial.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Trial
- Trial
- Artificial intelligence in cardiology: an evidence map across subspecialties, 2014-26.European heart journal. Digital health · 2026Article
- [Atrial fibrillation screening-reading the P-wave].Innere Medizin (Heidelberg, Germany) · 2026Review
- Article
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review.NPJ digital medicine · 2026Article
- Artificial Intelligence Powered Wearable and Portable Devices for Remote Cardiac Care and Population Health.Current cardiology reports · 2026Review
- Artificial intelligence in primary care: innovation at a crossroads.The Lancet. Primary care · 2026Review
- AI-ECG for early diagnosis of atrial fibrillation: an under-utilized tool for preemptive stroke prevention.Annals of medicine and surgery (2012) · 2026Article
- Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies.Cerebrovascular diseases extra · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- Are Machine Learning methods effective in detecting undiagnosed atrial fibrillation in primary care settings using electronic health records? A systematic review.PLOS digital health · 2025Article
- Systematic screening for atrial fibrillation with non-invasive devices: a systematic review and meta-analysis.The Lancet regional health. Europe · 2025Article
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative.BMC medical informatics and decision making · 2024Article
- A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation.Journal of personalized medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 9 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England. Methods and results: Eligible participants (aged ≥30 years without AF diagnosis; Conclusion: The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.
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Registered trials
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.