Evidence map›Paper›PMID 41866890›Full record

ReviewKorean circulation journal2026

Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.

Hak Seung Lee, Philip M Croon, Min Sung Lee, Timothy Poterucha, Chin Lin, Jeong Min Son, Ki-Hyun Jeon, Constantine Tarabanis, Seung-Pyo Lee, Kyung-Hee Kim and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Korean circulation journal, 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

12 authors.

Hak Seung LeeDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Korea.ORCID https://orcid.org/0000-0002-2200-6601
Philip M CroonSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.ORCID https://orcid.org/0000-0003-4490-8857
Min Sung LeeDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Korea.ORCID https://orcid.org/0000-0001-9247-2432
Timothy PoteruchaDivision of Echocardiography and Cardiac Imaging, Department of Cardiology, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0001-7284-3937
Chin LinSchool of Medicine, National Defense Medical Center, Taipei, Taiwan.ORCID https://orcid.org/0000-0003-2337-2096
Jeong Min SonDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Korea.ORCID https://orcid.org/0000-0001-7744-5735
Ki-Hyun JeonDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-6277-7697
Constantine TarabanisCardiology Division, Heart and Vascular Institute, Mass General Brigham, Boston, MA, USA.ORCID https://orcid.org/0000-0001-7563-2430
Seung-Pyo LeeDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5502-3977
Kyung-Hee KimDivision of Cardiology, Incheon Sejong Hospital, Incheon, Korea.ORCID https://orcid.org/0000-0003-0708-8685
Ambarish PandeyDivisions of Cardiology and Geriatrics, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA.ORCID https://orcid.org/0000-0001-9651-3836
Joon-Myoung KwonDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Korea.ORCID https://orcid.org/0000-0001-6754-1010

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence-enabled electrocardiography (AI-ECG) has rapidly advanced from experimental models to clinically deployed tools. This review outlines the evolution of AI-ECG across key domains including arrhythmia detection, structural heart disease diagnosis, and digital biomarker development. We summarize recent evidence from pragmatic randomized trials and prospective cohort studies that demonstrate the real-world utility of this approach across diverse populations and care settings. AI-ECG has demonstrated consistent accuracy in identifying conditions such as left ventricular systolic dysfunction, hypertrophic cardiomyopathy, and atrial fibrillation, with some studies reporting improved diagnostic rates, earlier intervention, and selected settings, reduced mortality. In addition to diagnostic support, AI-ECG enables longitudinal risk monitoring and screening for systemic diseases. Despite these advances, challenges remain around model generalizability, workflow integration, and regulatory adaptation. This review highlights both the clinical promise and the implementation hurdles of AI-ECG, underscoring the need for rigorous validation and thoughtful deployment to ensure its safe and effective integration into routine care.

Indexed as

Artificial intelligenceDeep learningDigital healthElectrocardiogramInnovation

Identifiers

PMID41866890
PMCPMC13017070

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.