ReviewNature reviews. Cardiology2021
Applications of artificial intelligence in cardiovascular imaging.
Review in Nature reviews. Cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
73 citing papers in PubMed, 1 synthesis or guideline pooled it, 151 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
- Computed tomography-guided vs conventional catheter ablation for ventricular tachycardia: the InEurHeart trial.European heart journal · 2026Trial
- Rationale and design of Dongzong CArdIovascuLar Bio-imaging RegistrY (DAILY) study: Bridging multiomics, imaging and cardiovascular disease.American journal of preventive cardiology · 2026Article
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Review
- Accuracy of machine learning models for mitral regurgitation severity assessment: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Artificial Intelligence-Enhanced Cardiac Point-of-Care Ultrasound: A Prospective Single-Arm Study.Mayo Clinic proceedings. Digital health · 2026Article
- Coronary stenosis assessment: AI-based CT quantification, visual analysis of invasive angiography, and quantitative coronary angiography.Insights into imaging · 2026Article
- Cardiovascular disease prevention in China: challenges and opportunities in the artificial intelligence-enabled digital health era.Nature reviews. Cardiology · 2026Review
- Three-dimensional visualization of arrhythmogenic substrate in mouse hearts using panoramic optical mapping and micro-computed tomography.Nature cardiovascular research · 2026Article
- Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.BMJ open · 2026Article
- Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Automated interpretation of fetal cardiac function evaluation from the echocardiogram.NPJ digital medicine · 2026Article
- Artificial Intelligence in heart and brain ultrasound: the least you need to know.Internal and emergency medicine · 2026Review
- AI-assisted multimodal assessment for right ventricular function from echocardiography predicts mortality in patients with pulmonary hypertension and right heart failure.Scientific reports · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Patellofemoral Instability: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Deep learning-assisted aortic stenosis detection and grading based on multiview versus single-view echocardiography.Quantitative imaging in medicine and surgery · 2025Article
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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What 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.