Letters to the Editor

Response to: Potential and limitations of large language models in acute chest pain triage

Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Received: 19 May 2026
Published: 10 July 2026
57
Views
21
Downloads

Authors

Dear Editor-in-Chief,

We would like to thank Shinde and colleagues for their thoughtful comments on our article, “Evaluating the predictive accuracy of ChatGPT in risk stratification for chest pain in the emergency department”.1,2 We are grateful for their appreciation of both the rationale of the study and the dynamic methodology adopted, in which ChatGPT was evaluated across sequential phases of the emergency department assessment.[...]

Downloads

Download data is not yet available.

1) Malalan F, Zaboli A, Fiore A, et al. Evaluating the predictive accuracy of ChatGPT in risk

stratification for chest pain in the emergency department. Emerg Care J 2025;21:13829 DOI: https://doi.org/10.4081/ecj.2025.13829

2) Shinde V, Kanani P, Bhimani K. Potential and limitations of large language models in acute chest pain triage. Response to Evaluating the predictive accuracy of ChatGPT in risk stratification for chest pain in the emergency department. Emerg Care J 2026;22:15130. DOI: https://doi.org/10.4081/ecj.2026.15130

3) Günay S, Öztürk A, Özerol H, Yiğit Y. Comparison of emergency medicine specialist, cardiologist, and ChatGPT in electrocardiography assessment. Am J Emerg Med 2024;80:51-60. DOI: https://doi.org/10.1016/j.ajem.2024.03.017

4) Günay S, Öztürk A, Yiğit Y. The accuracy of Gemini, GPT-4, and GPT-4o in ECG analysis: a comparison with cardiologists and emergency medicine specialists. Am J Emerg Med. 2024;84:68-73. DOI: https://doi.org/10.1016/j.ajem.2024.07.043

5) Rjoob K, Bond R, Finlay D, et al. Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications. Artif Intell Med 2022;132:102381. DOI: https://doi.org/10.1016/j.artmed.2022.102381

CRediT authorship contribution

Arian Zaboli, conceptualization; writing - review & editing; writing - original draft; Gianni Turcato, writing - review & editing; writing - original draft. 

 

Supporting Agencies

None

Data Availability Statement

Data available on request due to privacy/ethical restrictions.

How to Cite



Response to: Potential and limitations of large language models in acute chest pain triage. (2026). Emergency Care Journal. https://doi.org/10.4081/ecj.2026.15676