Large Language Models in Intracardiac Electrogram Interpretation: A New Frontier in Cardiac Diagnostics for Pacemaker Patients.

ABSTRACT:

Background

Interpreting intracardiac electrograms (EGMs) requires expertise that many cardiologists lack. Artificial intelligence models like ChatGPT-4o may improve diagnostic accuracy. This study evaluates ChatGPT-4o's performance in EGM interpretation across 4 scenarios (A-D) with increasing contextual information.

Methods

Twenty EGM cases from The EHRA Book of Pacemaker, ICD, and CRT Troubleshooting were analyzed using ChatGPT-4o. Ten predefined features were assessed in Scenarios A and B, while Scenarios C and D required 20 correct responses per scenario across all cases. Performance was evaluated over 2 months using McNemar's test, Cohen's Kappa, and Prevalence- and Bias-Adjusted Kappa (PABAK).

Results

Providing clinical context enhanced ChatGPT-4o's accuracy, im

SUBMITTER: Bozyel S 

PROVIDER: S-EPMC12503097 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature