ReviewCurrent cardiology reports2020
Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology.
Review in Current cardiology reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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
16 citing papers in PubMed.
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2025Review
- The most common errors in automatic ECG interpretation.Frontiers in physiology · 2025Article
- Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.Cell reports. Medicine · 2024Article
- Enhanced labor pain monitoring using machine learning and ECG waveform analysis for uterine contraction-induced pain.BioData mining · 2024Article
- Impact of Computer-Interpreted ECGs on the Accuracy of Healthcare Professionals.Current problems in cardiology · 2023Review
- Review
- What Else Can AI See in a Digital ECG?Journal of personalized medicine · 2023Review
- Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review.Biomedicines · 2023Review
- Artificial intelligence and atrial fibrillation.Journal of cardiovascular electrophysiology · 2022Review
- WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis.Frontiers in physiology · 2022Article
- Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring.Frontiers in cardiovascular medicine · 2022Article
- Deep learning and the electrocardiogram: review of the current state-of-the-art.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2021Review
- Machine Learning and Syncope Management in the ED: The Future Is Coming.Medicina (Kaunas, Lithuania) · 2021Review
- Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder.PloS one · 2021Article
- Sixty years of the Bruce protocol: reappraising the contemporary role of exercise stress testing with electrocardiographic monitoring.Porto biomedical journalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewTo (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field. RECENT
findingsAdvancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.
Indexed as
Identifiers
32562154What OpenQuestion holds
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.