This review examines approaches to creating digital twins of the cardiovascular system: mechanistic models with physiological interpretability but high computational costs, and data-driven methods with better scalability but limited robustness. Hybrid approaches, including physics-informed and graph-based methods, are described, along with validation challenges and pathways to clinical deployment.
Important for understanding the applicability limits and prospects of hybrid modeling methods in medicine, relevant to the development of digital twins of complex biological systems.
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