Advances in Electrocardiogram-Based Artificial Intelligence Reveal Multisystem Biomarkers.

Xichong Liu, Sabyasachi Bandyopadhyay, Albert J Rogers

Abstract read

In one paragraph

Article in Journal of clinical & experimental cardiology, 2025. 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

3 authors.

Xichong LiuDepartment of Cardiology, Cardiovascular Institute, Stanford University School of Medicine, Stanford, USA.

Sabyasachi BandyopadhyayDepartment of Cardiology, Cardiovascular Institute, Stanford University School of Medicine, Stanford, USA.

Albert J RogersDepartment of Cardiology, Cardiovascular Institute, Stanford University School of Medicine, Stanford, USA.

Funding

Electrical Mapping Signatures of Adverse Structural and Functional Remodeling in Ventricular ArrhythmiaK23HL166977 · NHLBI · STANFORD UNIVERSITY · PI Albert Joseph Rogers · 2023 to 2026

$680k

American Heart Association-American Stroke Association 23CDA933663American Heart Association-American Stroke Association 25POST1361932NHLBI NIH HHS K23 HL166977

6 · The paper itself

Abstract

As Artificial Intelligence (AI) plays an increasingly prominent role in society, its application in clinical cardiology is gaining traction by providing innovative diagnostic, prognostic, and therapeutic solutions. Electrocardiogram (ECG), as a ubiquitous diagnostic tool in cardiology, has emerged as the leading data source for Deep Learning (DL) applications. A recent study from our group used ECG-based DL model to identify cardiac wall motion abnormalities and outperformed expert human interpretation. Motivated by this work and that of many others, we aim to discuss advances, limitations, future directions, and equity considerations in DL models for ECG-based AI applications.

Indexed as

Artificial intelligenceDeep learningElectrocardiogram (ECG)Health disparities

Identifiers

PMID40443717

PMCPMC12121951

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.