Key result
Cardiac digital twins personalize risk prediction and optimize therapy but high costs demand targeted clinical use.
Why the study?
Precision cardiac electrophysiology seeks tailored healthcare strategies, and digital twins integrating patient-specific data with mechanistic simulations are fundamental to this approach.
Cardiac digital twins offer personalized mechanistic insights that could optimize therapy planning, such as guiding ablation in atrial fibrillation, though cost-effectiveness and clinical implementation remain challenging.
Answers are grounded in this paper and cite their sources.
May guide ablation planning in persistent AF; leaves open whether AI precision medicine proves more cost-effective.
INTRODUCTION: Precision cardiac electrophysiology seeks to provide healthcare strategies tailored to the individual patient. The concept of digital twins is fundamental to this approach. Digital twins in cardiac electrophysiology are personalized computer models that replicate the electrical activity of patients' hearts. By integrating patient-specific data with mechanistic simulations, they can predict disease risk and help optimize therapy planning. AREAS COVERED: This narrative review summarizes the state-of-the-art of cardiac digital twins, including advances in anatomical and functional twinning, as well as illustrative case studies. It then discusses key technical/clinical challenges, focusing on model development, data recording and integration, and pathways toward clinical implementation. EXPERT OPINION: Fueled by growing data availability and advances in computational power, the vision of cardiac digital twins is moving from theoretical concept to potential clinical reality. For instance, digital twins could guide ablation in patients with persistent atrial fibrillation by identifying potential targets through computer simulations. However, given the potentially substantial costs, identifying patient groups and clinical applications in which digital twins provide the greatest value is essential. In parallel, artificial intelligence-based precision medicine may offer a more cost-effective and efficient alternative for certain scenarios, while digital twins likely remain most valuable when mechanistic insight is required.
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Buonocunto et al. (2026) conducted a review in Cardiac arrhythmias. Digital twins was evaluated. Cardiac digital twins offer personalized models to predict disease risk and optimize therapy, though their substantial costs require identifying specific clinical applications for greatest value.
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