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Cardiovascular Digital Twins: From Physics to Data
US & West ·
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.
Why it matters
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.
The National University of Singapore (NUS) and the Society of Algorithmic Intelligence (SoAI) is hosting IntelligenceX 2026: Global Quantum × AI Frontier, from 24 to 26 September 2026 at NUS University Town, focused on advances at the intersection of quantum computing and artificial intelligence…
A pre-registered study with 317 participants compared AI-moderated, human-moderated and static interviews. AI moderation matched human depth, covered more themes and, at fixed budget, surfaced more customer needs; digital twins built from interview data were validated against held-out responses to marketing stimuli.