Artificial Intelligence-Based Automated Interpretation of Images of Electrocardiograms: Development and Multinational Validation of ECG-GPT.

ABSTRACT:

Background

Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows rely on computerized ECG interpretation tools built into ECG signal acquisition systems, which use rule-based algorithms that are unreliable and frequently not available in low-resource settings. We developed and validated a format-independent vision encoder-decoder model - ECG-GPT - that can generate free-text, expert-level interpretations directly from 12-lead ECG images.

Methods

Using 12-lead ECGs and their corresponding diagnosis statements collected at the Yale-New Haven Health System (YNHHS) between 2000 and 2022, we developed a vision-text transformer model to generate interpretation statements from images of E

SUBMITTER: Khunte A 

PROVIDER: S-EPMC10889032 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature