Cardiovascular care and AI
Digital twin is a virtual patient model which is simulation that is upating lively through interaction with wearable device data, EHR, ECG, CT, MRI, Echo, and genes. Its major difference with previous AI prediction model is that it is not just merely a prediction, but a comparison of multiple possible scenarios through simulating those scenarios virtually.
Currently, digital twin is used in cardiovascular disease in these ways:
- Disease Phenotyping: Characterization of heterogeneity in
- Cardiomyopathies
- Cardiac arrhythmias
- Heart failure
- Procedural Planning: Percutaneous coronary intervention (PCI)
- Coronary artery bypass grafting (CABG)
- Transcatheter aortic valve replacement (TAVR)
- Atrial fibrillation and ventricular tachycardia ablation
- Risk Prediction: Sudden cardiac death
- Arrhythmia recurrence
- Electrophysiological and Hemodynamic Simulation: Cardiac conduction
- Myocardial contraction
- Blood flow dynamics
The review envisions a transition from:
“Trial-and-error clinical decision-making” → “Simulation-driven personalized care”
Examples include:
- Selecting drug A vs. drug B based on virtual patient response
- Identifying CRT lead positions that maximize ventricular remodeling
- Choosing anti-arrhythmic agents most likely to terminate arrhythmia efficiently
This research shows that AI healthcare technology can assist docters effectively in patient management
