ArticleDigital health
Artificial intelligence (ChatGPT) ready to evaluate ECG in real life? Not yet!
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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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Who cites it
5 citing papers in PubMed.
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Artificial Intelligence for Biomedical Diagnostics: Diagnostic Accuracy and Reliability of Multimodal Large Language Models in Electrocardiogram Interpretation.Life (Basel, Switzerland) · 2026Article
- Comparative evaluation of multimodal large language models for diagnostic accuracy in pediatric electrocardiography: a prospective comparative diagnostic accuracy study.European journal of pediatrics · 2026Article
- Can ChatGPT compete with emergency medicine specialists? A two-stage assessment of ECG interpretation supported by clinical information.BMC emergency medicine · 2026Article
- Comparative evaluation of artificial intelligence chatbots for real-time guidance during intraoperative anesthetic crises: A simulation-based study.Saudi journal of anaesthesiaArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims at evaluating if ChatGPT-based artificial intelligence (AI) models are effective in interpreting electrocardiograms (ECGs) and determine their accuracy as compared to those of cardiologists. The purpose is therefore to explore if ChatGPT can be employed for clinical setting, particularly where there are no available cardiologists. Methods: A total of 107 ECG cases classified according to difficulty (simple, intermediate, complex) were analyzed using three AI models (GPT-ECGReader, GPT-ECGAnalyzer, GPT-ECGInterpreter) and compared with the performance of two cardiologists. The statistical analysis was conducted using chi-square and Fisher exact tests using scikit-learn library in Python 3.8. Results: Cardiologists demonstrated superior accuracy (92.52%) compared to ChatGPT-based models (GPT-ECGReader: 57.94%, GPT-ECGInterpreter: 62.62%, GPT-ECGAnalyzer: 62.62%). Statistically significant differences were observed between cardiologists and AI models ( Conclusions: ChatGPT-based models have potential in ECG interpretation; however, they currently lack adequate reliability beyond oversight from a doctor. Additionally, further studies that would improve the accuracy of these models, especially in intricate diagnoses are needed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.