ArticleAnatolian journal of cardiology2025
Large Language Models in Intracardiac Electrogram Interpretation: A New Frontier in Cardiac Diagnostics for Pacemaker Patients.
Article in Anatolian journal of cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Reply to Letter to the Editor: "Translating Multimodal Intelligence into Cardiac Diagnostics: A Critical Perspective on Large Language Model-Assisted Electrogram Interpretation".Anatolian journal of cardiology · 2026Article
- AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios.Medical sciences (Basel, Switzerland) · 2026Article
- Translating Multimodal Intelligence into Cardiac Diagnostics: A Critical Perspective on Large Language Model-Assisted Electrogram Interpretation.Anatolian journal of cardiology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterpreting 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.
methodsTwenty 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).
resultsProviding clinical context enhanced ChatGPT-4o's accuracy, improving from 57% (Scenario A) to 66% (Scenario B). "No Answer" rates decreased from 19.5% to 8%, while false responses increased from 8.5% to 11%, suggesting occasional misinterpretation. Agreement in Scenario A showed high reliability for atrial activity (κ = 0.7) and synchronization (κ = 0.7), but poor for chamber (κ = -0.26). In Scenario B, understanding achieved near-perfect agreement (Prevalence-Adjustment and Bias-Adjustment Kappa (PABAK) = 1), while ventricular activity remained unreliable (κ = -0.11). In Scenarios C (30%) and D (25%), accuracy was lower, and agreement between baseline and second-month responses remained fair (κ = 0.285 and 0.3, respectively), indicating limited consistency in complex decision-making tasks.
conclusionThis study provides the first systematic evaluation of ChatGPT-4o in EGM interpretation, demonstrating promising accuracy and reliability in structured tasks. While the model integrated contextual data well, its adaptability to complex cases was limited. Further optimization and validation are needed before clinical use.
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