ArticleJournal of community hospital internal medicine perspectives2024
Role of Artificial Intelligence-assisted Decision Support Tool for Common Rhythm Disturbances: A ChatGPT Proof-of-concept Study.
Article in Journal of community hospital internal medicine perspectives, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Diagnostic accuracy of ChatGPT for 12-lead ECG-based localisation of ventricular ectopic foci prior to catheter ablation.Frontiers in medicine · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The objective of this article was to explore the use of ChatGPT as a clinical support tool for common arrhythmias. Methods: This study assessed the feasibility of using ChatGPT as an AI decision-support tool for common rhythm disturbances. The study was conducted using retrospective data collected from electronic medical records (EMRs) of patients with documented rhythm disturbances. The model's performance was evaluated using sensitivity, specificity, positive predictive value, and negative predictive value. Results: A total of 20,000 patients with rhythm disturbances were included in the study. The ChatGPT model demonstrated high diagnostic accuracy in identifying and diagnosing common rhythm disturbances, with a sensitivity of 93%, specificity of 89%, positive predictive value of 91%, and negative predictive value of 92%. The ROC curve analysis showed an area under the curve (AUC) of 0.743, indicating the excellent diagnostic performance of the ChatGPT model. Conclusion: The model's diagnostic performance was comparable to clinical experts, indicating its potential to enhance clinical decision-making and improve patient outcomes. Clinical trial registration: Not applicable.
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