Prof. Dr. Björn Eskofier
The concept of Digital Twin has recently come to increasing popularity enabled by technological
advances in Big Data analysis, Internet of Things (IoT) solutions, cloud computing, the availability of processing power, and cheap, miniaturized sensor technology [4, 9]. The Digital Twin concept
was first mentioned in 2012 by NASA and the U.S. Air Force. Their team described the Digital
Twin as “an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or
system that uses the best available physical models, sensor updates, fleet history, etc., to mirror
the life of its corresponding flying twin” (p. 7) . They intended to improve their spacecrafts and
thereby depicted the possible future of vehicle design and product lifecycle management. Hence,
the Digital Twin is a digital representation of a real object or person that comprises heterogeneous
data sources and types.
Up to now, there are many approaches to describe and analyze the advantages and necessity of a
Digital Twin application in Industry 4.0, production optimization and product lifecycle management [9, 11, 12]. However, the focus on the production perspective narrows down the potential of
a Digital Twin. Beyond this single perspective, various implementations exist that address Digital
Twins in different domains: Infrastructure of the British railway , electricity transmission over
the whole country realized in Finland  and a City Digital Twin created alongside its construction in India .
Modeling humans as a Digital Twin has created a new emerging field. A digital Twin for a human
would contain and integrate all data concerning this individual. This approach is widly discussed as a solution to further advance digital healthcare . Some existing approaches in this field
already use the Digital Twin concept to model human organs [7, 8]. However, the potential of a Patient Digital Twin goes even beyond modelling human organs. Technical enablers like continuous
communication and connectivity, miniature sensors, and IoT solutions provide us with more data
than ever before. Using rapidly advancing Machine Learning algorithms and Big Data analysis
to deliver continually improving results, allow us to generate insights besides disease treatment.
With a Digital Twin system the focus of our current healthcare system has the potential to shift
from a “disease management” approach to an “improved quality of life and preventive healthcare”
point of view . The concept to profit from an interconnective and self-contained Patient Digital
Twin could be stretched to a holistic human-centered Digital Twin concept to interact as intuitive
human digital representative in an increasingly digitized world. A Digital Twin will then act as an
anchor for data controllability and guarantees ongoing tracing and value-adding for the individual,
resulting in a fair trade model of data value.
The goal of this thesis is to analyze current applications and value drawings of Digital Twins.
Furthermore, the limitations of current concepts are examined and compared to the potential of a
holistic approach to a Digital Twin system with focus on patients. Additionally data sovereignty
as a key element for a Digital Twin application in healthcare is investigated. Based on qualitative
expert interviews with different involved stakeholders, a baseline evaluation of requirements for a
healthcare oriented Digital Twin is created. Potential challenges of the system design, such as data
security, data rights and ownership, the current legal basis, informed consent, and data storage
models are reviewed. Finally, a concept for a human-centered Digital Twin is derived.
In a user experience study a patient’s interaction with a model of the derived patient-centered healthcare system is investigated regarding questions about informed consent and users’ acceptance
of the new perspective on data control and data sovereignty. The conclusions that can be drawn
are concerning the patient’s level of feeling informed and the created locus of control. The result
of this work could help to define a universal concept of a Digital Twin in the context of healthcare
systems. Additionally this work could define principles or rules that help to improve usability and
the user’s acceptance regarding the development of Digital Twins.
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 K. Bruynseels, F. Santoni de Sio, J. van den Hoven. Digital Twins in Health Care: Ethical
Implications of an Emerging Engineering Paradigm. Frontiers in Genetics, 9:31, 2018.
 E. Glaessgen, D. Stargel. The Digital Twin Paradigm for Future NASA and U.S. Air Force
Vehicles. 53rd Structures, Structural Dynamics and Materials Conference. Honolulu, Hawaii, 2012.
 S. Haag, R. Anderl. Digital Twin – Proof of Concept. Manufacturing Letters, 15:64-66,
 S. Kaewunruen, N. Xu. Digital twin for sustainability evaluation of railway station buildings. Frontiers in Built Environment 4:77, 2018.
 E. Leclerc. Digital funding of the new green field capital of Andhra Pradesh: technotopian
ideas and spatial justice in Amaravati. Urban Challenges in a complex World-Digital cities
and spatial justice 2017 IGU Urban Commission Annual Meeting, 2017.
 T. Mansi, T. Passerini, D. Comaniciu. Artificial Intelligence for Computational Modeling
of the Heart. Academic Press, London, UK, 1. edition, 2019.
 R. Martinez-Velazquez, R. Gamez, A. El Saddik. Cardio Twin: A Digital Twin of the human
heart running on the edge. In 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA)), Istanbul, Turkey, pages 1-6, 2019.
 Q. Qi, F. Tao. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0:
360 Degree Comparison. IEEE Access, 6:3585-3593, 2018.
 F. Tao, M. Zhang, A.Y.C. Nee. Digital Twin Driven Smart Manufacturing. Academic Press,
London, UK, 1. edition, 2019.
 T. Uhlemann, C. Schock, C. Lehmann, S. Freiberger, R. Steinhilper. The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems. Procedia
Manufacturing, 9:113-120, 2017.
 T. Uhlemann, C. Lehmann, R. Steinhilper. The Digital Twin: Realizing the Cyber-Physical
Production System for Industry 4.0. Procedia CIRP, 61:335-340, 2017.