ReviewArrhythmia & electrophysiology review2020
Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.
Review in Arrhythmia & electrophysiology review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.EBioMedicine · 2023Pooled it
- Artificial Intelligence in Echocardiography for Acute Coronary Syndromes: Current Evidence and Integration With Electrocardiography and Biomarkers.Echocardiography (Mount Kisco, N.Y.) · 2026Review
- Evoked potentials in stroke rehabilitation: current applications, emerging technologies, and future directions.Frontiers in neuroscience · 2026Review
- Review
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Evaluating the understanding of the ethical and moral challenges of Big Data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study.BMC medical ethics · 2024Article
- Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study.European heart journal. Digital health · 2024Article
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data.Open heart · 2023Article
- Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.European heart journal · 2023Article
- Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks.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 · 2022Article
- Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encodersEuropean heart journal. Digital health · 2022Article
- Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk.European heart journal. Digital health · 2022Article
- Role of Digital Health During Coronavirus Disease 2019 Pandemic and Future Perspectives.Cardiac electrophysiology clinics · 2022Review
- Big Data in electrophysiology.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis.International journal of arrhythmia · 2022Review
- Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening.Frontiers in cardiovascular medicine · 2022Article
- Artificial Intelligence, Superintelligence and Intelligence.Arrhythmia & electrophysiology review · 2021Article
- Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms.European heart journal. Digital health · 2021Article
- Clinical Utility of Body Surface Potential Mapping in CRT Patients.Arrhythmia & electrophysiology review · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.
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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.