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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Introduction
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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
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Updated: Sep 16, 2025

Impact of Intracardiac Neurons on Cardiac Electrophysiology and Arrhythmogenesis in an Ex Vivo Langendorff System
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Large Language Models in Intracardiac Electrogram Interpretation: A New Frontier in Cardiac Diagnostics for Pacemaker

Serdar Bozyel1, Ahmet Berk Duman1, Şadiye Nur Dalgıç1

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Summary

Artificial intelligence like ChatGPT-4o shows promise in interpreting intracardiac electrograms (EGMs), with accuracy improving with clinical context. However, complex cases remain a challenge, requiring further validation for clinical use.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Intracardiac electrogram (EGM) interpretation demands specialized expertise often lacking among cardiologists.
  • Advanced AI models, such as ChatGPT-4o, present a potential avenue for enhancing diagnostic accuracy in EGM analysis.
  • This research systematically assesses ChatGPT-4o's capabilities in interpreting EGMs under varying levels of contextual information.

Purpose of the Study:

  • To evaluate the diagnostic performance of ChatGPT-4o in interpreting intracardiac electrograms (EGMs).
  • To assess the impact of increasing clinical context on ChatGPT-4o's EGM interpretation accuracy and reliability.
  • To compare ChatGPT-4o's performance across different scenarios with varying complexity.

Main Methods:

  • Twenty intracardiac electrogram (EGM) cases were analyzed using ChatGPT-4o.
  • Performance was evaluated across four scenarios (A-D) with increasing contextual data.
  • Statistical analysis included McNemar's test, Cohen's Kappa, and Prevalence- and Bias-Adjusted Kappa (PABAK) over two months.

Main Results:

  • ChatGPT-4o's accuracy improved from 57% (Scenario A) to 66% (Scenario B) with added clinical context.
  • Agreement was high for atrial activity and synchronization in Scenario A, but poor for chamber identification.
  • Performance in complex scenarios (C and D) was lower, with fair consistency over time, indicating limitations in complex decision-making.

Conclusions:

  • ChatGPT-4o demonstrates promising accuracy and reliability for structured EGM interpretation tasks.
  • The AI model effectively integrates contextual information but shows limited adaptability to complex clinical cases.
  • Further research and validation are necessary before widespread clinical adoption of AI in EGM interpretation.