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A roadmap for the development of human body digital twins

Nature Reviews Electrical Engineering volume 1, pages 199–207 (2024)Cite this article

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Abstract

A digital twin (DT) of the human body is a virtual representation of an individual’s physiological state, created using real-time data from sensors and medical devices, with the purpose of simulating, predicting and optimizing health outcomes through advanced analysis and modelling. Human body DTs have the potential to revolutionize healthcare and wellness, but their responsible and effective implementation requires consideration of multiple intertwined engineering aspects. This Perspective presents an overview of the status and prospects of the human body DT and proposes a five-level roadmap to guide its development, from the sensing components, in the form of wearable devices, to the data collection, analysis and decision-making systems. The support, security, cost and ethical considerations that must be addressed are also highlighted. Finally, we provide a framework for the development and a perspective on the future of the human body DT, to aid interdisciplinary research and solutions for this evolving field.

Key points

  • Human body digital twins (DTs) can be modelled following a five-level approach.

  • Wearable sensor technologies and algorithms are needed to capture human data and build the DTs.

  • Support, security, cost and ethics must be considered to ensure responsible and effective implementation of the human body DTs.

  • Limitations and prospects of human body DTs are related to the need for efficient computational architectures, and their effective integration in clinical settings for personalized diagnostics and treatments.

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Fig. 1: Five-level roadmap for human body digital twins (DTs).
Fig. 2: Must-have technologies to build human body digital twins.
Fig. 3: Application trend of human body digital twins (DTs) across various model levels.

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Acknowledgements

L.G.O., C.T. and W.Y. acknowledge funding from the UK Engineering and Physical Sciences Research Council (grants EP/W024284/1, EP/P027628/1, EP/K03099X/1, EP/L016087/1 and EP/L015889/1). E.O. was supported by the UKRI Centre for Doctoral Training in AI for Healthcare (grant EP/S023283/1). S.G. and Y.D. acknowledge funding from the National Natural Science Foundation of China (grants 62171014 and 61803017).

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Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Cambridge, UK

    Chenyu Tang, Wentian Yi & Luigi G. Occhipinti

  2. School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Bejing, China

    Chenyu Tang, Yanning Dai & Shuo Gao

  3. UKRI Centre for Doctoral Training in AI for Healthcare, Department of Computing, Imperial College London, London, UK

    Edoardo Occhipinti

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All authors researched data for the article, contributed substantially to discussion of the content and wrote the article. L.G.O. reviewed and edited the manuscript before submission.

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Correspondence to Shuo Gao or Luigi G. Occhipinti.

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Nature Reviews Electrical Engineering thanks Ali Ahmad Malik and Issam El Naqa for their contribution to the peer review of this work.

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Tang, C., Yi, W., Occhipinti, E. et al. A roadmap for the development of human body digital twins. Nat Rev Electr Eng 1, 199–207 (2024). https://doi.org/10.1038/s44287-024-00025-w

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