Evidence map›Paper›PMID 34534279›Full record

ReviewEuropean heart journal2021

Application of artificial intelligence to the electrocardiogram.

Zachi I Attia, David M Harmon, Elijah R Behr, Paul A Friedman

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European heart journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
158citing papers in PubMed, 2 pooled it
–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

158 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. Autonomous Agents for Auditable Cardiovascular Artificial Intelligence Development.medRxiv : the preprint server for health sciences · 2026
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98 more citing papers are in PubMed but not listed here.

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

4 authors.

Zachi I AttiaDepartment of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.ORCID 0000-0002-9706-7900
David M HarmonDepartment of Internal Medicine, Mayo Clinic School of Graduate Medical Education, 200 First Street SW, Rochester, MN 55905, USA.ORCID 0000-0002-8102-3380
Elijah R BehrCardiology Research Center and Cardiovascular Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George's University of London and St. George's University Hospitals NHS Foundation Trust, Blackshaw Rd, London SW17 0QT, UK.ORCID 0000-0002-8731-2853
Paul A FriedmanDepartment of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Indexed as

Atrial FibrillationCOVID-19Artificial IntelligenceElectrocardiographyHumansSARS-CoV-2Artificial intelligenceDigital healthElectrocardiogramsMachine learning

Identifiers

PMID34534279
PMCPMC8500024

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.