ArticleCardiovascular digital health journal
A comprehensive artificial intelligence-enabled electrocardiogram interpretation program.
Article in Cardiovascular digital health journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Determinants of ECG misdiagnosis of atrial fibrillation or flutter in the emergency department and their clinical implications: results from a single-center observational study.BMC emergency medicine · 2026Observational
- Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT.European heart journal. Digital health · 2026Article
- A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.Scientific reports · 2026Article
- Challenges in the Classification of Cardiac Arrhythmias and Ischemia Using End-to-End Deep Learning and the Electrocardiogram: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development and evaluation of an artificial intelligence-based electrocardiogram prediction model for emergency chest pain patients.Frontiers in medicine · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Accuracy of machine electrocardiogram interpretation and implementation of a de-prioritization protocol in the emergency department.World journal of emergency medicine · 2025Article
- The Heart-Brain Axis in the Artificial Intelligence Era: Integrating Old and New Insights Towards New Targeting and Innovative Neuro- and Cardio-Therapeutics.International journal of molecular sciences · 2025Review
- A deep foundation model for electrocardiogram interpretation: enabling rare disease detection through transfer learning.European heart journal. Digital health · 2025Article
- Validation of a handheld electrocardiogram 6 lead recorder to obtain chest lead equivalents: An Africa Heart Rhythm Association study.Heart rhythm O2 · 2025Article
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Machine-Learning for Phenotyping and Prognostication of Myocardial Infarction and Injury in Suspected Acute Coronary Syndrome.JACC. Advances · 2024Article
- Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care.European heart journal. Digital health · 2024Article
- Performance of Prehospital ECG and Impact on Prehospital Service Time: Comparison between EMT-II and EMT-P Teams.Acta Cardiologica Sinica · 2024Article
- Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.Current cardiology reports · 2024Review
- Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study.European heart journal. Digital health · 2024Article
- Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.JACC. Advances · 2023Article
- Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Emerging role of artificial intelligence in cardiac electrophysiology.Cardiovascular digital health journal · 2022Review
Corrections and comments
- Erratum issuedErratum.2021
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Automated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence-enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists. Methods: We developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist's final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients ≥18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n = 1,749,654), validation (n = 249,951), and testing (n = 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm's performance to the cardiologist's interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves. Results: The model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of ≥0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity ≥95% for all codes. Conclusions: An AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.
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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.