Reliability of Artificial Intelligence-enhanced Electrocardiography.

ABSTRACT:

Background

The scientific literature on artificial intelligence-enabled electrocardiography (AI-ECG) has defined a robust performance of AI models in detecting and predicting several structural heart disorders (SHDs) using ECGs. However, as a diagnostic test, the real-world utility of AI-ECG reliability requires the consistency of its results when repeated under similar conditions.

Aim

To evaluate the reliability of AI-ECG models for different ECGs for the same person, across different diagnostic labels, and using varied modeling approaches.

Methods

We used ECG images (2000-2024) from 5 hospitals and an outpatient network within a large, integrated US health system. For each individual, we identified multiple ECGs recorded within a 30-day period. We evaluated 7 models

SUBMITTER: Dhingra LS 

PROVIDER: S-EPMC12637772 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature