1 Introduction
We read with great interest the article by Accorsi et al. titled “Evaluation of AI-enhanced tele-ECG response time and diagnosis in acute chest pain patients,” recently published in Frontiers in Cardiovascular Medicine (2025) (1). The authors present a valuable real-world analysis of an AI-assisted tele-electrocardiography (tele-ECG) system in emergency settings, highlighting its potential to improve response times and diagnostic efficiency. While the study offers promising insights into the application of artificial intelligence in telemedicine, several methodological aspects warrant further clarification to strengthen the validity and generalizability of the findings.
2 Lack of a control group or comparative analysis
A primary concern is the absence of a control group or comparative arm without AI support. Although the authors note that the observed response times are shorter than those reported in previous studies, the lack of a direct within-study comparison limits causal inference regarding the AI's specific contribution. A randomized or matched design comparing AI-assisted versus conventional tele-ECG interpretation would provide more robust evidence of the AI system's incremental benefit, as demonstrated in recent trials such as the ARISE study (2).
3 Limited characterization of the AI model
While the study briefly describes the convolutional neural network (CNN) architecture and its internal validation metrics, key details regarding the model's training dataset, external validation, and performance across different demographic or clinical subgroups are not provided. Ensuring transparency throughout the development of AI models by detailing data provenance, labeling methodologies, and inherent biases serves as a cornerstone for achieving reproducibility and building essential trust in clinical settings (3, 4).
4 Incomplete reporting of ECG interpretation workflow
The study does not specify whether the same cardiologists interpreted ECGs both with and without AI support, nor does it detail how the AI output was integrated into the final report. To fully appreciate the system's operational role and limitations, it is necessary to clarify the human-AI interaction process. This entails defining whether the AI acted merely as a prioritization tool or also played a part in shaping the diagnostic decisions themselves, a consideration of paramount importance in light of the established inter-rater variability in ECG interpretation (5).
5 Underrepresentation of clinical context
The analysis focuses exclusively on ECG tracings and response times, with limited integration of clinical data such as patient symptoms, risk factors, or outcomes. This restricts the ability to assess the AI's impact on clinical decision-making or patient-oriented endpoints (for example, mortality, revascularization success). Future studies should aim to link ECG findings with longitudinal outcomes to evaluate the AI's prognostic utility, as emphasized in recent tele-ECG meta-analyses (6).
6 Variability in ECG quality and exclusions
The authors appropriately excluded 12.58% of tracings due to artifacts or technical issues. However, the impact of these exclusions on the overall diagnostic accuracy and workflow efficiency is not discussed. A sensitivity analysis including borderline or suboptimal tracings could provide insight into the system's robustness in real-world conditions, especially given known challenges in pre-hospital ECG transmission (7).
7 Generalizability and implementation context
The study was conducted within a well-structured telemedicine network in Brazil. The applicability of these findings to settings with less infrastructure or different patient populations remains unclear. Reporting on barriers to implementation, cost-effectiveness, and scalability would enhance the translational value of the research, particularly for low-resource regions where tele-ECG is most needed (8, 9).
In conclusion, Accorsi et al. have made a noteworthy contribution to the growing body of evidence supporting AI-enhanced telemedicine. Their findings suggest that AI can expedite ECG interpretation and support diagnostic workflows in resource-limited settings. However, to fully establish the clinical utility and reliability of such systems, future studies should incorporate controlled comparisons, detailed model reporting, and broader clinical validation. We commend the authors for their work and hope these considerations will inform subsequent research in this rapidly evolving field.
Statements
Author contributions
YD: Writing – review & editing, Writing – original draft, Supervision.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
1.
Accorsi TAD Pitta FG Rompkoski J Moreira FT Morbeck RA Köhler KF et al Evaluation of AI-enhanced tele-ECG response time and diagnosis in acute chest pain patients. Front Cardiovasc Med. (2025) 12:1532770. 10.3389/fcvm.2025.1532770
2.
Lin CL Chang TW Chang CH Lee CH Hsing SC Fang WH et al Artificial intelligence-powered rapid st-elevation myocardial infarction identification via electrocardiogram (ARISE): a pragmatic randomized controlled trial. NEJM AI. (2024) 1:AIoa2400190. 10.1056/AIoa2400190
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Attia ZI Harmon DM Behr ER Friedman PA . Application of artificial intelligence to the electrocardiogram. Eur Heart J. (2021) 42(46):4717–4730. 10.1093/eurheartj/ehab649
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Chang KC Hsieh PH Wu MY Wang YC Wei JT Shih ESC et al Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram. Eur Heart J Digit Health. (2021) 2(2):299–310. 10.1093/ehjdh/ztab029
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Kwok CS Bennett S Azam Z Welsh V Potluri R Loke YK et al Misdiagnosis of acute myocardial infarction: a systematic review of the literature. Crit Pathw Cardiol. (2021) 20(3):155–162. 10.1097/HPC.0000000000000256
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Marcolino MS Maia LM Oliveira JAQ Melo LDR Pereira BLD Andrade-Junior DF et al Impact of telemedicine interventions on mortality in patients with acute myocardial infarction: a systematic review and meta-analysis. Heart. (2019) 105(19):1479–1486. 10.1136/heartjnl-2018-314539
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Schwaab B Katalinic A Richardt G Kurowski V Krüger D Mortensen K et al Validation of 12-lead tele-electrocardiogram transmission in the real-life scenario of acute coronary syndrome. J Telemed Telecare. (2006) 12(6):315–8. 10.1258/135763306778558204
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Ncube B Mars M Scott RE . Recommendations for developing a telemedicine strategy for Botswana: a meta-synthesis. Int J Environ Res Public Health. (2023) 20(18):6718. 10.3390/ijerph20186718
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Sera T Otani N Bannai H Hasegawa T Umemura T Honda H et al The current status of emergency departments in secondary emergency medical institutions in Japan: a questionnaire survey. Int J Emerg Med. (2023) 16(1):40. 10.1186/s12245-023-00513-0
Summary
Keywords
acute chest pain, AI-assisted diagnosis, clinical integration, methodological evaluation, tele-ECG
Citation
Diao Y (2026) Commentary: Evaluation of AI-enhanced tele-ECG response time and diagnosis in acute chest pain patients. Front. Cardiovasc. Med. 13:1753595. doi: 10.3389/fcvm.2026.1753595
Received
25 November 2025
Revised
09 January 2026
Accepted
19 January 2026
Published
02 February 2026
Volume
13 - 2026
Edited by
Hendrik Tevaearai Stahel, University Hospital of Bern, Switzerland
Reviewed by
Giovanni Corrado, Valduce Hospital, Italy
Updates
Copyright
© 2026 Diao.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yingying Diao diaoying.1986@163.com
Disclaimer
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.