To cite this version: Jorge Luis Rojas-Arce, Eduardo Cassiel Ortega-Maldonado. The Advent of the Digital Twin: A
Prospective in Healthcare in the Next Decade. IFIP International Conference on Advances in Pro-
duction Management Systems (APMS), Sep 2021, Nantes, France. pp.249-255, 10.1007/978-3-030-
85910-7_26. hal-03806497 Distributed under a Creative Commons Attribution 4.0 International License Jorge Luis Rojas–Arce1[0000−0003−3697−8353] and Eduardo Cassiel
Ortega–Maldonado2 Jorge Luis Rojas–Arce1[0000−0003−3697−8353] and Eduardo Cassiel
Ortega–Maldonado2 1 Facultad de Ingenier´ıa, Departamento de Ingenier´ıa en Sistemas Biom´edicos,
Universidad Nacional Aut´onoma de Mexico, Mexico City, Mexico
jorge.rojas.arce@comunidad.unam.mx
2 Facultad de Ingenier´ıa, Departamento de Ingenier´ıa en Sistemas Biom´edicos,
Universidad Nacional Aut´onoma de Mexico, Mexico City, Mexico
cassiel.ortega@comunidad.unam.mx 1 Facultad de Ingenier´ıa, Departamento de Ingenier´ıa en Sistemas Biom´edicos,
Universidad Nacional Aut´onoma de Mexico, Mexico City, Mexico
jorge.rojas.arce@comunidad.unam.mx
2 Facultad de Ingenier´ıa, Departamento de Ingenier´ıa en Sistemas Biom´edicos,
Universidad Nacional Aut´onoma de Mexico, Mexico City, Mexico
cassiel.ortega@comunidad.unam.mx 1 Facultad de Ingenier´ıa, Departamento de Ingenier´ıa en Sistemas Biom´edicos,
Universidad Nacional Aut´onoma de Mexico, Mexico City, Mexico
jorge.rojas.arce@comunidad.unam.mx
2 Facultad de Ingenier´ıa, Departamento de Ingenier´ıa en Sistemas Biom´edicos,
Universidad Nacional Aut´onoma de Mexico, Mexico City, Mexico
cassiel.ortega@comunidad.unam.mx Abstract. Industry 4.0 is facing a fast–development in its technologies,
automatizing and restructuring processes in order to make them data–
driven. However, its uses are not exclusive for the industry as its name
suggests, since it can be be extended to health sector. In the next decade,
the Digital Twin (a digital replica of a physical asset) is going to face a
spread into mainstream market due to its potential applications in the
healthcare sector, such as a 24/7 monitoring of the evolution of cancer
and its treatment, a heart based on cloud–computing, an even receive
medical treatment that adapts to every person. But before seeing this as
a reality, there are several obstacles that the researchers need to overcome
for the proposed Digital Twins to reach its full–potential. Keywords: Digital Twin · Healthcare · Prospective. HAL Id: hal-03806497
https://inria.hal.science/hal-03806497v1
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abroad, or from public or private research centers. Distributed under a Creative Commons Attribution 4.0 International License This document is the original author manuscript of a paper submitted to an IFIP 
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may be some differences in the official published version of the paper. Such 
differences, if any, are usually due to reformatting during preparation for publication or 
minor corrections made by the author(s) during final proofreading of the publication 
manuscript. This document is the original author manuscript of a paper submitted to an IFIP 
conference proceedings or other IFIP publication by Springer Nature. As such, there 
may be some differences in the official published version of the paper. Such 
differences, if any, are usually due to reformatting during preparation for publication or 
minor corrections made by the author(s) during final proofreading of the publication 
manuscript. 1
The Digital Twin and its common misconceptions As a result of the upcoming Industry 4.0, technologies are being quickly devel-
oped and growing their popularity in both specialized ﬁelds and mainstream
media alike, being these technologies are the Internet–of–Things (IoT), cloud
computing, big data and the automation that does not require the human inter-
vention as a consequence of the implementation of machine learning algorithms
and artiﬁcial intelligence. As it has been stated, their potential applications
are not only limited to the industry, but can be extended and successfully im-
plemented into the healthcare systems around the world, modifying how we
understand and work with them [7]. The state–of–the–art of this revolution is known as the Digital Twin (DT),
a concept that uses and encompasses all the previously mentioned technologies
to develop a living model in the form of a digital instance that represents and
mirrors in real time its physical asset or system. However, this term is so broad
that most of the time it may lead to false impressions of what it really is, aspect
that will be addressed up–ahead, being the main distinction between them how
the data ﬂows from the physical to the digital object and vice–versa [5]. J. L. Rojas–Arce, E. C. Ortega–Maldonado 2 1.2
Digital Shadow This instance, as its name may suggest, is only a digital representation of a
physical object, meaning that data is only automated in a one–way ﬂow from
the physical to the digital object [6]. It is mostly used to gather and integrate
information from diﬀerent sources to facilitate real–time analysis [12]. 1.1
Digital Twin Summarizing, a DT can be described as a virtual replica of a physical object with
the virtue of fully–automated data acquisition [7]. In other words, a change that
is made in the digital asset is reﬂected immediately in its physical counterpart,
and vice–versa. The main diﬀerence of the DT, is that there is no need for a
manual data input [5]. 1.3
Digital Model This is where the majority of the misconceptions are centred. This case is a
virtual replica of the physical object and it requires a full–time manual input of
the data [7]. They are used for the most part in the industry to understand how
a change in the digital object would aﬀect its counterpart if implemented [6]. 2.1
IT Infrastructure For this proposal to be successful, then it is mandatory to have the means that
will ensure the optimal performance of the system. Both Artiﬁcial Intelligence
and Machine Learning are expected to be the key–players [11], and both require
high–performing hardware and software to be in a constant analysis of data,
aspect that becomes obvious in the high–running costs, as well in its subsequent
environmental impact [5]. [ ]
From the IoT point–of–view, the main issue is the ﬂow of data, problem that
is enlarged with the rise of big data, which means that a gargantuan volume of
data is in constant transit. However, this data, most of the time, is in a “raw”
state that needs to be prepared, sorted and organized before being useful for
any research [5]. In the healthcare sector, there exists a high–natural variability,
which is explained by the innate diﬀerences of the human body, such as age,
height, or response to medical treatments; parameters that need to be understood
by the sensors implemented in the DT to prevent false-positives [9]. As a result, before a personalized, data–driven healthcare becomes a real-
ity, the IT infrastructure needs to be leveraged. This new architecture needs
to comply with diﬀerent standards that ensure the well–being of the patient
and continuous function of the DT, its design must entail a hybrid platform:
Centralized for a straightforward management and eﬀortless data sharing, yet
distributed enough to ensure real–time deployment [7]. 2
Challanges to Overcome Before seeing these DTs as a reality and as a part of our daily lives, there are
some obstacles that need to be dealt with. The ﬁrst, and most challenging one,
is to design them to be identical to their physical self, to be indistinguishable
from each other; that is, a real–time exchange of information that is both easy
to be operated by its user, and at the same time to be complex enough to model
and answer sudden changes in real time [11]. 1.4
Digital Terms Summary To synthesize the presented information, and as way to clarify any possible
doubts that may appear, Table 1 is presented as a mean to understand the ﬂow
of the data between the physical and digital objects, as well their most common
applications. Table 1. Diﬀerences between the Digital Terms
Digital Term
Flow of data
Applications
Digital Twin
Fully
automatic
between
the physical and digital as-
set, and vice–versa [7]. Real–time changes in an ob-
ject are manifested in the
other as well [5]. Digital Shadow
Automatic from the physi-
cal to the digital counter-
part; manual from the dig-
ital to the physical asset [6]. Used for information gath-
ering and eventual analysis
[12]. Digital Model
Completely manual between
the physical and digital as-
set, and vice–versa [7]. Used to describe possible
changes [6]. Table 1. Diﬀerences between the Digital Terms A Prospective in Healthcare in the Next Decade 3 2.2
Data Privacy and Security In the recent years, the threat of cyberattacks has been increasing, compromising
personal data that ranges from personal keywords to paralyze whole communi-
ties’ ﬁnances. In the case of the DT, the data used is both sensitive and conﬁ-
dential, and it must be only accessed by the patient and by qualiﬁed personnel,
such as physicians or nurses. In the case of IoT, interconnected devices are an
easy target for denial–of–service attacks (DDoS) that can take oﬄine a health-
care system, temporarily freezing its infrastructure and even pose a threat to the
lives of the patients [8]. As ludicrous as it might sound, in the next decades the
idea of hijacking a organ can become a reality if cybersecurity is not guaranteed. A
f An important factor to be consolidated in both DT and Industry 4.0 is law
regulation, governments need to deﬁne the scope of the Artiﬁcial Intelligence and
ensure that the data is at all–times protected [5]. As a byproduct of the massive J. L. Rojas–Arce, E. C. Ortega–Maldonado 4 amount of information generated by the big data, cyber–attacks are prone to
happen, compromising sensitive data [7], meaning that cybersecurity analysis
and protocols are mandatory, and security frameworks are to be adopted as a
design paradigm known as “Security by Design” [8]. 2.3
Data Quality As expected for a complex system that integrates and coordinates multiple en-
gineering ﬁelds, the DT needs to work with noise–free and high–quality data to
perform as expected [5]. In the case of the DT, data are crucial, and they come from multiple sources
and need to be integrated as a whole. The sources of this information are many,
some is obtained from the digital–physical asset, other from the digital–models
that support the DT performance, and a small yet crucial part is provided by
experts in the ﬁelds of health and engineering. In the end, all of them need to be
merged together (applying diﬀerent AI methods) to give as a result the “fusion
data”, core of the DT performance [10]. 2.4
Ethics A downside of introducing a data–driven healthcare system, is the that the
boundaries of medicine need to be posed once more. The use of the DT, at
the beginning, raises the possibility of a new kind of patient, ones that have
a digital enhancement of themselves. Of course, personalized medicine highly
exceeds the cost of oﬀ–the–shelf treatments, meaning that only people with
the enough resources can access to this opportunity. As a result of both these
situations, a segmentation of the population and which medical treatment they
receive is prone to occur if the technologies used do not lower costs [3]. Another issue that needs to be addressed before the DT can go mainstream,
is the ownership of the data used, being the most important ones “Who will be
the rightful owner? The patient or a health institution?”, “Who will play the
bigger role, the government or the private sector and for what purpose they will
use personal data? Will all they have a share?” Although these questions at ﬁrst sound like science–ﬁction, the truth is that
they need to be answered as soon as possible, the DT is around the corner and
its development must not be left stray [3]. 2.6
Challenges Summary 2.6
Challenges Summary Table 2. Summary of the challenges that the DTs need to overcome
Challange
Description
Possible Solution
IT Infrastruc-
ture
-Need of high–performing hard-
ware and software [5]. -Volume of data in a “raw” state
[5]. -Natural variability in each pa-
tient[9]. IT infrastructure needs to be lever-
aged and comply with diﬀerent
standards [7]. Data Privacy
and Security
-Threat of cyberattacks, specially
DDoS [8]. -Unregulated technology [5]. Adopt
protocols
and
security
frameworks [8]. Data Quality -High–grade of prepared data [5]. -Data integration from multiple
sources [10]. Data merge using AI methods [10]. Ethics
-Question the current boundaries
of medicine [3]
Inequality between patients [3]. -Ownership of the data
Lower costs and deﬁne the roles of
all the involved parties [3]. Expectations
Change of current trends [5]. Adapt and understand the techno-
logical limits [5]. 3
Future Applications Although the applications in both products and services seem endless, it is time
to focus solely on what can be made in the next decade with this brand–new tech-
nology. At the time being, the DT has already been successfully implemented at
the healthcare sector. Its ﬁrst use was in medical devices for preventive mainte-
nance and for the optimization of its capabilities [1]. However, the asset potential
can be exploited to disrupt how we understand and perceive human healthcare
and lifestyle and make a fully–functional device of ourselves, thus creating a new
paradigm in health [5]. 2.5
Expectations Albeit the potential applications of the DT, it is expected to change how the
problems are solved in the healthcare sector. Instead, caution is needed to really
understand that the implementation of the DT can take its time to become a
reality, due to the fact that all the supporting technologies and the DT itself is
in a developing phase where the real applications remain uncertain [13]. Another important aspect to take into consideration, is that current trends
may not be the same for the next decade, meaning that the DT applications in
healthcare may stem from what is proposed and described in this paper [5]. A Prospective in Healthcare in the Next Decade 5 3.2
Personalized Medicine As a result of the fast–development that the DTs are showing in the healthcare
sector, the old paradigm of “one size ﬁts all”, i.e. assume the people’s physiology
is exactly the same for everybody, is meant to disappear. Instead, personalized
medicine is the next standard, as it can monitor all day long the patient and
gather data on how the treatment performs: If the dose needs to be modiﬁed,
which side–eﬀects are expected in the patient, and even if the treatment will be
eﬀective or not [4]. As it has been stated, data from multiple sources are collected by the DT
need to be concealed in order to perform as desired. In turn, this is the biggest
obstacle for the personalized medicine, since it deals with multiple degrees of
complexity in a single individual, that ranges from the molecular to even the
environmental levels. Another beneﬁt of this brand–new approach, is that some
diseases can be diagnosed in early stages in which damage and symptoms to
the patient are yet to occur, giving physicians the enough time to reverse the
disease, if it is the case [2]. 3.3
Telemedicine Subsequent from the IoT and Cloud computing of the DT, another applica-
tion for it is the telemedicine. Using the data that is always ﬂowing and being
analyzed, it can allow for a real–time monitoring and prediction of the health–
status of the patient, meaning that face–to–face interactions between patient
and physician can be reduced. As a result of this characteristic, healthcare platforms are almost certain to
occur in the next decade, a digital landscape where diﬀerent services can be
oﬀered: Medication reminder, crisis warning, and the disease prevention or the
early detection of them. Of course, the data used is conﬁdential, and it needs to
be protected by security frameworks and protocols at all times, and as discussed
previously, data leakage must be prevented [7]. 3.1
Non-Invasive Treatment The most realistic application for the next decade of non–invasive treatments
is not the whole human body, but the development of physiological models of
organs or systems. For example, Dassault Syst`emes in 2015 released its “Liv-
ing Heart” project, which is the ﬁrst digital representation of the organ that
works as the real one: The electrical impulses that are translated into mechanic
contractions and expansions and control the blood ﬂow [1]. J. L. Rojas–Arce, E. C. Ortega–Maldonado 6 For the 5G/6G communications era, the previous application is expected to
improve and to be capable to replicate a disease and its eﬀects in a DT. As a
result, research is being made to develop the DT of lung cancer in all of its stages
and the damage that these organs undergo, and is expected for this approach
to beneﬁt from the enormous amount of data that circulates between the digital
and physical organ, then made understandable for the the physicians and ﬁnally
choose the best medical treatment for this patient; process that is also known
as personalized medicine [14]. 4
Conclusions As history shows, when a revolutionary technology is ﬁrst presented, most of
the people fear the unknown until they saw the potential and beneﬁts in their A Prospective in Healthcare in the Next Decade 7 lifestyles. It is expected that during the next decade, healthcare is most likely to
face severe changes on how we understand it, due to the advent of the Digital
Twin, a concept that is currently undergoing a continuous research and devel-
opment process to bring together health specialists and engineers, as part of the
Fourth Industrial Revolution. Whilst there are serious obstacles to overcome before seeing this as a pub-
lic technology, the beneﬁts that it oﬀers for the healthcare greatly exceed the
costs. For the next decade, biomedical engineering is expected to make bigger
developments and become a key player in the redeﬁnition of medicine, one that
is personalized and solely data–driven. 14. Zhang, J., Li, L., Lin, G., Fang, D., Tai, Y., Huang, J.: Cyber Resilience in Health-
care Digital Twin on Lung Cancer. IEEE Access 8, 201900–201913 (2020) 8
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