Human Body Digital Twin: A Master Plan
Not on the map. Why?
Abstract (arXiv)
A human body digital twin (DT) is a virtual representation of an individual's physiological state, created using real-time data from sensors and medical test devices, with the purpose of simulating, predicting, and optimizing health outcomes through advanced analytics and simulations. The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors. This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development. The roadmap covers the development of various components, such as wearable devices, data collection, data analysis, and decision-making systems. The article also highlights the necessary support, security, cost, and ethical considerations that must be addressed in order to ensure responsible and effective implementation of the human body DT. The proposed roadmap provides a framework for guiding future development and offers a unique perspective on the future of the human body DT, facilitating new interdisciplinary research and innovative solutions in this rapidly evolving field.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 10 of its 72 references that are in the map. First posted on arXiv 18 Jul 2023.
Builds on
10 of 72 references.
References · 10
- 2017Attention Is All You Needcited by 59,932
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Emergent Abilities of Large Language Modelscited by 2,483
- 2020Interpretability of machine learning based prediction models in healthcarecited by 75
- 2021Brain-inspired computing: We need a master plancited by 51
- 2021Medical Imaging and Machine Learningcited by 12
- 2020Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomographycited by 8
- 2020In-Ear Measurement of Blood Oxygen Saturation: An Ambulatory Tool Needed To Detect The Delayed Life-Threatening Hypoxaemia in COVID-19cited by 6
Built on by
Citing papers per year: 2024: 4; 2025: 1.
5 papers.
- 2024A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily lifecited by 1
- 2024An AI-Driven Multimodal Smart Home Platform for Continuous Monitoring and Assistance in Post-Stroke Motor Impairmentcited by 1
- 2024AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationshipscited by 0
- 2024Wearable intelligent throat enables natural speech in stroke patients with dysarthriacited by 0
- 2025Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotioncited by 0
- 2025Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion16 Dec 2025
- 2024An AI-Driven Multimodal Smart Home Platform for Continuous Monitoring and Assistance in Post-Stroke Motor Impairment28 Nov 2024
- 2024Wearable intelligent throat enables natural speech in stroke patients with dysarthria27 Nov 2024
- 2024A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life1 Aug 2024
- 2024AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships8 Jul 2024