Building Digital Twins of Different Human Organs for Personalized Healthcare
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908770675720192 |
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| author | Lyu, Yilin Li, Zhen Tran, Vu Yang, Xuan Li, Hao Wang, Meng Cheng, Ching-Yu Bhat, Mamatha Jirsa, Viktor Foo, Roger Lim, Chwee Teck Li, Lei |
| author_facet | Lyu, Yilin Li, Zhen Tran, Vu Yang, Xuan Li, Hao Wang, Meng Cheng, Ching-Yu Bhat, Mamatha Jirsa, Viktor Foo, Roger Lim, Chwee Teck Li, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11318 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building Digital Twins of Different Human Organs for Personalized Healthcare Lyu, Yilin Li, Zhen Tran, Vu Yang, Xuan Li, Hao Wang, Meng Cheng, Ching-Yu Bhat, Mamatha Jirsa, Viktor Foo, Roger Lim, Chwee Teck Li, Lei Medical Physics Image and Video Processing Tissues and Organs 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. |
| title | Building Digital Twins of Different Human Organs for Personalized Healthcare |
| topic | Medical Physics Image and Video Processing Tissues and Organs |
| url | https://arxiv.org/abs/2601.11318 |