ReviewEuropean heart journal2024
Cardiovascular care with digital twin technology in the era of generative artificial intelligence.
Review in European heart journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technologies, Clinical Applications, and Implementation Barriers of Digital Twins in Precision Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- A digital twin-enhanced decision support system improves time-in-range in type 1 diabetes: a randomized clinical trial.Scientific reports · 2025Trial
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Exercise in Patients with Subclinical Atherosclerosis: Mechanisms, Clinical Evidence, and Practical Recommendations.Current atherosclerosis reports · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Heart Failure sub-phenotyping and in-hospital and 28-day mortality prediction based on mean arterial pressure trajectory modeling.American heart journal plus : cardiology research and practice · 2026Article
- Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework.Healthcare (Basel, Switzerland) · 2026Review
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Wearable Electrocardiogram Technologies for the Early Detection of Acute Coronary Syndromes.JACC. Asia · 2026Review
- Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation.Journal of arrhythmia · 2026Review
- Sex-specific assumptions underlie cardiovascular digital twin technologies: A narrative review.PLOS digital health · 2026Review
- Progress in Understanding Brain Injury After Cardiopulmonary Bypass in Cardiac Surgery: A Narrative Review.Reviews in cardiovascular medicine · 2026Review
- The multi-modal digital heart: From future perspective to challenges.Journal of translational internal medicine · 2026Article
- MorphiNet: A Graph Subdivision Network for Adaptive Bi-Ventricle Surface Reconstruction.IEEE transactions on medical imaging · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- From Parallel Programming to Bidirectional Crosstalk: The Brain-Kidney Axis in Cardiovascular-Kidney-Metabolic Syndrome.Antioxidants (Basel, Switzerland) · 2026Review
- Innovative strategies for early detection of cardiotoxicity: artificial intelligence and multi-modality collaborative models.Journal of thrombosis and thrombolysis · 2026Review
- Non-invasive Computational Techniques for Diagnosing Myocardial Ischemia: Challenges and Future of FFRAnnals of biomedical engineering · 2026Review
- Artificial Intelligence Powered Wearable and Portable Devices for Remote Cardiac Care and Population Health.Current cardiology reports · 2026Review
- Conversational AI for remote monitoring in heart failure: a prospective controlled pilot study.European heart journal. Digital health · 2026Article
3 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.
Funding
Abstract
Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.
Indexed as
Identifiers
What Socratic 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 Socratic graph.