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August 24, 2026Frontiers in Cardiovascular MedicineOpen Access

Artificial intelligence–enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models

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Why the study?

AI applied to the standard 12-lead ECG is being developed to screen for LVSD and triage for confirmatory testing, but synthesis across settings and evaluation of emerging foundation models are needed.

Does artificial intelligence-enabled electrocardiography improve the detection of left ventricular systolic dysfunction in patients across various clinical settings?

Design

Review

Key result

AI-enabled electrocardiography demonstrates high discrimination for reduced ejection fraction across care settings, though prospective trials are needed to establish clinical benefit.

Authors

ABAndreas BollmannVPVictoria PradlerDHDaniela Husser

Discussion

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Overview

May aid low-LVEF detection across settings; leaves open prospective echocardiography validation of foundation models.

Structured PICO

Does artificial intelligence-enabled electrocardiography improve the detection of left ventricular systolic dysfunction in patients across various clinical settings?

P
Population
A review summarizing the diagnostic performance, external validation, and pragmatic implementation of AI-ECG for left ventricular systolic dysfunction across various clinical settings.
I
Intervention
Artificial intelligence-enabled 12-lead electrocardiography (AI-ECG)
O
Outcome
Detection of left ventricular systolic dysfunction (LVSD) / reduced ejection fraction

AI-enabled 12-lead ECG serves as a scalable, effective screening tool for left ventricular systolic dysfunction across multiple care settings, facilitating targeted echocardiography and early diagnosis.

Limitations

  • Performance attenuation in the presence of atrial fibrillation and wide QRS complexes
  • Much of the evidence base remains retrospective and sensitive to population mix
  • Lack of prospective outcome-focused trials demonstrating clinical benefit
  • Potential for false positives leading to downstream testing and patient anxiety
  • Signal-format compatibility and quality gating meaningfully affect real-world yield
  • Requires confirmatory imaging given prevalence-dependent positive predictive value
  • Prospective echocardiography-anchored validation is required before broader deployment of foundation models

Cite This Study

Bollmann et al. (2026) conducted a review in Left ventricular systolic dysfunction. Artificial intelligence-enabled electrocardiography (AI-ECG) vs. Standard care was evaluated. AI-enabled electrocardiography demonstrates high discrimination for reduced ejection fraction across care settings, though prospective trials are needed to establish clinical benefit.

synapsesocial.com/papers/6a95766525f7ee1b8233250dhttps://doi.org/10.3389/fcvm.2026.1899224
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