Diagnostic accuracy of ChatGPT for 12-lead ECG-based localisation of ventricular ectopic foci prior to catheter ablation.

Background: Precise pre-procedural localisation of ventricular ectopic (VE) foci shortens mapping time, reduces fluoroscopy, and improves ablation success. Large language models such as ChatGPT offer instant, free-text clinical support; however, their accuracy in ECG-based VE localisation is unknown. Methods: In this single-centre pilot study, we assessed the diagnostic accuracy of ChatGPT in 50 consecutive adults (average age: 43 ± 14 years; 58% women) scheduled for first-time VE ablation. ChatGPT served as the index test, and invasive electroanatomical mapping during the ablation served as the reference standard. A blinded electrophysiologist converted each index 12-lead ECG into a structured textual description of QRS morphology. ChatGPT-4o (temperature 0.2) was then tasked with assigning one of five anatomical origins (RVOT, LVOT, papillary muscle, fascicular, and epicardial). Predictions were compared with electro-anatomical mapping during catheter ablation, and agreement was measured using Cohen's Results: Electro-anatomical mapping identified 30 RVOT, 11 LVOT, 4 papillary, 1 fascicular, and 4 epicardial foci. ChatGPT correctly localised 17/50 cases (34%), yielding an overall Cohen's κ of -0.02 (95% CI -0.18 to 0.14). Sensitivity/specificity was 40%/55% for the RVOT and 36%/62% for the LVOT; no fascicular or epicardial origins were correctly predicted. The performance of ChatGPT did not differ based on the presence of structural heart disease ( Conclusion: Freetext querying of ChatGPT failed to provide clinically meaningful VE localisation, performing no better than chance and markedly below published ECG-based algorithms. This likely reflects the model's lack of domain-specific training and its reliance on purely text-based reasoning without direct access to ECG signals. Current general-purpose language models should not be relied upon for procedural planning in VE ablation; future work must integrate multimodal training and domain-specific optimisation before LLMs can augment electrophysiology practice.