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arXiv · 2601.11318

Building Digital Twins of Different Human Organs for Personalized Healthcare

Yilin Lyu·Zhen Li·Vu Tran·Xuan Yang·Hao Li·Meng Wang·Ching-Yu Cheng·Mamatha Bhat·Viktor Jirsa·Roger Foo·Chwee Teck Lim·Lei Li
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Abstract

Digital twins are virtual replicas of physical entities and are poised to transform personalized medicine through the real-time simulation and prediction of human physiology. Translating this paradigm from engineering to biomedicine requires overcoming profound challenges, including anatomical variability, multi-scale biological processes, and the integration of multi-physics phenomena. This survey systematically reviews methodologies for building digital twins of human organs, structured around a pipeline decoupled into anatomical twinning (capturing patient-specific geometry and structure) and functional twinning (simulating multi-scale physiology from cellular to organ-level function). We categorize approaches both by organ-specific properties and by technical paradigm, with particular emphasis on multi-scale and multi-physics integration. A key focus is the role of artificial intelligence (AI), especially physics-informed AI, in enhancing model fidelity, scalability, and personalization. Furthermore, we discuss the critical challenges of clinical validation and translational pathways. This study not only charts a roadmap for overcoming current bottlenecks in single-organ twins but also outlines the promising, albeit ambitious, future of interconnected multi-organ digital twins for whole-body precision healthcare.

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Yilin Lyu, Zhen Li, Vu Tran, Xuan Yang, Hao Li, Meng Wang, Ching-Yu Cheng, Mamatha Bhat, Viktor Jirsa, Roger Foo, Chwee Teck Lim, Lei Li. 2026-01-16. Building Digital Twins of Different Human Organs for Personalized Healthcare. https://arxiv.org/abs/2601.11318

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