Digital Twins

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A digital twin is a digital representation of a living or non-living physical entity. It can be of a physical asset, process, person, place or system of devices. Integrating internet of things, AI, machine learning algorithms and many other emerging technologies, a digital twin allows for a digital living simulation model that continuously updates when the physical asset changes. This can provide many different benefits to an organization,such as more accurate decision making, decreased costs and safer working environments. Digital twins have applications in many different industries, such as manufacturing, automotive and healthcare. Many organizations have started integrating digital twins in their process as it is estimated that half of organizations implementing IoT (Internet of Things) have plans to also implement Digital Twins. [1]


Constructing a Digital Twin

There are two parts of a digital twin: the Digital Master and the Digital Shadow. Developers must first build the digital master of the twin. The digital master is the digital model of the physical asset, created by either researching the physics that underlie the physical entity or by using a prototype of the physical counterpart [2]. It can include geometry models, part lists or variant configurations of the physical asset. Sensors are then placed on the physical entity, which then seamlessly transmits data of the physical entity to the digital master. The digital shadow is the data that is obtained over the lifecycle of the physical asset and is collected from the sensors. The combination of the Digital Master and Digital Shadow create the Digital Twin.

Levels of a Digital Twin

Component Level – the lowest level of digital twin created of a specific piece of equipment within a production line. This can be as small as a bearing, but usually will be of a piece of equipment which has a significant impact on the performance of the asset it is a part of.

Asset Level – digital twin created of a highly critical, single component within the manufacturing process, such as an engine or pump. Asset level twins provide visibility at the equipment level.

System Level– digital twin created of a collection of assets that are interrelated, such as an oil refinery.

Process Level – the highest level of digital twin created that provides a view of a set of systems or operations such as a whole manufacturing process. It can be informed by a set of assets but the primary focus of a digital twin at the process level is on the processes itself rather than its assets. [3]

Digital Twin Platform

One of the key benefits of a digital twin is the ability to monitor complex systems, such as a manufacturing line or smart buildings. These complex systems require digital twins at the component level, asset level and system level, requiring hundreds or even thousands of different digital twins. Together they form the digital twin platform. The digital twin platform enables the scalability of the digital twin operations but there are several requirements for a digital twin platform to exist.[4]

Manage the Digital Twin Lifecycle – A digital twin platform must have the ability to bring together the various inputs needed to first create the digital master for the digital twin. The digital twin platform must also have the ability to manage any changes to the digital master, such as any software updates that may occur. When there is a digital twin of a manufacturing line, there may be hundreds of different digital masters, and the digital twin platform must have the tools to test, deploy and manage each digital twin based on their digital master. [4]

Single Source of Truth – Often, the physical state of an asset can change, due to environmental changes, maintenance, software updates, etc. A digital twin platform must be able to update continuously to provide the exact state of each individual twin.[4]

Open API – A digital twin platform must provide an open API that allows any system to interact with the digital twins. For example, the digital twins need to be able to interact with machine learning and analytics services through an API. Organizations need to be able to have their digital twins integrated into other enterprise systems such as ERP or SCM systems. [4]

Visualization and Analysis – Organizations need to be able to create visualizations, dashboards, and in depth analyses of live data that is derived from their digital twin platform. This live data is linked to the digital master which allows for organizations to drill down into the component level of the digital twin platform.[4]

Event and process management – Organizations need to be able to setup events and business processes based on digital twin data. For example, an accurate view of the current maintenance status of a physical asset so that scheduled maintenance calls can be automatically scheduled based on live data. [4]

Customer and user perspective – Collaboration needs to be enabled between the stakeholders and the digital twin. Definitions need to be set based on which organization owns the digital twins, who can access the data and information derived from the digital twin sensors, and how data can safely be shared with outside users. [4]

Benefits of Digital Twinning

Enhanced Predictions

With the sensors on the physical assets, digital twins allow for larger data collection and provides a deeper understanding of an assets behavior. It allows decision makers to look closer at conditions and events that cause changes in the asset. With machine learning algorithms, scenarios can be created for how the asset will perform in certain situations, which can potentially alleviate decreases in performance or failures before they happen. Projections can be made on the longevity and reliability of an asset. [5] For example, if a wind farm implemented digital twin technology, they would gather data on the amount of energy generated for a year, and would then be able to predict energy generation over the next several years by simulating different future events using the digital twin. [4]

Cost Reductions

With a better understanding of how the physical asset works and when it needs maintenance or replacement, maintenance of the asset can be optimized and cost savings generated. For example, wind farms can monitor damages that have occurred to turbine wings during certain events such as windstorms or tornadoes. They can then simulate the same amount of damage using the digital twin and predict the amount of damage needed to cause the turbine wings to break, in order to determine the average lifespan of a turbine wing. Turbine wings can then be repaired before they break down, in order to reduce the amount of downtime.[4]

Safety Monitoring

One of the most dangerous industries in the world is the construction sector. More than 4,000 construction workers died on construction site between 2008-12 according to the Bureau of Labor Statistics. Digital twins allow construction companies to track hazardous places on the site, dangerous activities and the use of materials. Digital twins can provide information to help develop a system of early notification of dangerous activities. Having a digital twin of the construction site can also allow for easier access to emergency services, who would be able to easily navigate the site as well as have a current list of workers on site. [6]

Digital Twins in Manufacturing

Digital Twinning in The Manufacturing Industry

Digital Twins in the manufacturing industry centers on the concept of data and information being transferred between physical objects and their virtual representations [7]. For example, for a construction site, there would be both the physical site and a digitally mapped, virtual site incorporated together. The virtual space functions by pulling in data from the physical building using live data feeds from different sensors in the building to project the real-time status of the building online.


Disruptions at the Industry level

The manufacturing industry is one of the earliest adopters of digital twins, along with being one of the most applicable forms of digital twinning exemplified in the workplace. Most, if not all construction and development companies now use digital twinning that range in complexity from simple sensors like air quality sensors in air ducts, to complex designs in a manufacturing plant. In the past, companies in the industry relied heavily on labour intensive inspections of their entire operation, such as construction sites or commercial buildings. They would have to hire different specialists to come in and observe different machinery, fixtures and other items that would have experienced wear and tear through everyday use. This process would take weeks of maintenance and service shutoffs to accommodate the inspections, which would not only interrupt experience for clients and guests, but also the high costs associated with hiring specialists.

In present day, most companies have incorporated technology and digital twinning into their daily operations. Companies will now use the concept of digital twins on almost any machine, tool, and object that experiences wear and tear. There would likely be a live data feed from all these objects automatically syncing and collecting data from building inspections or through data retrieval from sensors in pipes, air ducts, doorways and other fixtures. Improvements in technology have also made hardware and software much more inexpensive, thereby, increase the desire from manufacturing companies to incorporate these products into their operations.

Example of Digital Twins in Manufacturing

Formula 1

F1 Logo

Formula 1 (F1) has been on the forefront of data analytics and digital twins. Teams have been forced to resort to realistic online simulations because of the rules and structure of the league. The Formula 1 governing body has structured a racing weekend to include only two days of on-circuit racing, meaning that teams do not have a chance to practice at any specific circuit aside from the two designated days. This structure has inevitably forced teams to incorporate sensors and data into their vehicles in order to gather as much information about a circuit within the limited time slot. After collecting the data over the two days, teams would adjust the parts and system settings on the vehicle to give them the leading edge on race day. On race day, the amount of data transferred between the sensors on an F1 vehicle and its engineering team can be anywhere from two terabytes of data and above [8]. Furthermore, F1 vehicles have roughly 150 different live sensors that measure components such as: engine performance, tire health, brake wear, and body biometrics . Not to mention, all the data being sent to the engineering time is being sent at a refresh rate of 0.001 seconds [9]. Aside from race performances, engineers also use the data when crafting new parts for the vehicle, as they produce over 3000 different components every week in order to continuously stay ahead of its rivals [10]. Before producing said parts, teams will first test out the virtual structure of items like shocks, breaks, and nose wings first in order to make sure that they are built to their optimal capacity. Once the items are digitally levelled, the manufacturing team will commence the process of physically creating the new parts.


NASA Control Centre Celebrating Apollo 13's Return

NASA was a pioneer of digital twinning in the early 1970’s. Scientists at NASA used “pairing technology”, a rudimentary system before the advancements in modern data sensors. When the infamous oxygen explosion occurred on the Apollo 13 spacecraft, NASA engineers used pairing technology to replicate the computers, buttons, and switches on the spacecraft, with a physical replica inside the command center [11]. In modern missions, NASA’s technology is far beyond the simple replica system they used during the Apollo program, like F1, their systems now follow a protocol where any new physical machinery must be first tested as a digital entity before being physically produced [12].

Microsoft Azure and Chevron

Chevron and Microsoft Partnership

Digital twinning is not only limited to building developments either, with the rise of global competition amongst firms, companies can no longer avoid the importance of integrating technology into their supply chain. In the past, cutting-edge technology was considered risky, unstable and costly; however, in order to stay ahead of its competitors, companies like Chevron have had to look into new technologies like digital twinning to help save long term operating costs. In 2017, Chevron and Microsoft Azure, partnered together to “digitize its oil fields and accelerate deployment of new technologies that can increase revenues, reduce costs and improve the safety and reliability of operations” [13]. Chevron’s Chief Information Officer (CIO), Bill Braun, further expands on this partnership by saying that in the past Chevron would have to figure out and guess how their equipment was doing, but in the future, the equipment itself will tell us how it is performing. [14] Microsoft Azure will be providing Chevron engineers the ability to access all their data in one localized cloud repository, versus the older system that had different individual silos that slowed down progression.[15] They plan to do this by implementing hundreds of sensors in its oil machinery equipment with the purpose of providing predictive maintenance by the end of this year. If this test program is successful, they plan to implement the IOT software into all of its machinery globally.

Challenge Advisory: Automotive Case Study

Sample digital twin interface

A case study was done by Challenge Advisory on the effects of implementing digital twins in an automotive production process. Current manufacturing processes of an unnamed car manufacturer was at 14-17 hours per car. The implementation of the digital twin in the manufacturing process included 3 distinctive features. First, AI powered machine learning was integrated giving developers the ability to predict and avoid failure conditions. Second, the removal of data aggregation, allowing for continuous data stacking even when the digital twin was untethered from the physical assets. Third, the database centric recommendations, where decisions were fully automated based on the large amounts of data collected. [4]

Digital twin sensors were not only put on the manufacturing line, but into many of the core components of the car, such as the engines. Software was installed onto the car manufacturer’s computing devices responsible for controlling the main processes of manufacturing, giving the digital twin the ability to monitor machinery temperature, performance speed, and power input/output. Every move of the manufacturing equipment was able to be tracked which reduced electricity consumption and decreased production times. [4]


After the study was conducted, it was found that the digital twins were able to optimize the manufacturing process and reduce the production time from 14-17 hours down to 9-10 hours. This enabled the organization to increase their profit margin per car by 41%. The digital twins allowed for the optimization of the movement of manufacturing equipment by shortening the distance tools needed to travel in order to complete the desired outcome. The real time data that digital twins provide also reduced downtime of the equipment by 37%, as the digital twins could predict when machine parts would break down, and could be replaced during downtime. [4]

Example of Digital Twin Software

Invicara, a company founded in 2013, is a digital twin-based software that specializes in the start to finish process of building development. They first start off by creating a collaborative project management hub that allows different players in the planning and constructing timeline to present and share information between parties in the hub. Managers and project managers will be able to monitor the status of projects and see necessary or related files to a certain fixtures within the building. The digital twin portion becomes the focus point once the building has been completed, and through the use of IoT. The building would have sensors in strategic locations that would help monitor and send feedback to the “digital copy” of the building [16]. This process allows for the elimination of guesswork for building managers when deciding when to hire building inspectors.


Different possibilites within Invicara below:


Difference between Digital Twins and 3D modelling

A common misconception occurs often when comparing digital twins and 3D modelling. Users often confuse the 3D models that architects and building developers use when planning a structure, with live virtual copies of physical sites. Architects and developers would normally utilize a similar software called Building Information Modelling (BIM), which focuses primarily on the visualization and 3D modeling of a physical building [17]. Digital twinning however, differs from simple 3D modelling as it has more emphasizes on the data and information that is being transferred between the physical and digital building rather than simple modelling.


Smart Cities

Digital twin model of a city. Picture Source[18]

A smart city is a municipality that uses sensors to collect data that is used to derive insights in order to efficiently manage its assets, resources and services. Data can be used for crime detection, waste management, schools water supplies, monitoring and managing traffic systems, and much more. The International Data Corporation (IDC) projects that smart city initiatives will reach US$95.8 billion in 2019. With the development of smart cities, the smart city sensors have allowed for the construction of a digital twin of the city. The digital twin of the city can be used to visualize the pulse of the city in real time, with layered data utilities, buildings, movement of people or vehicles and urban infrastructure. By having this real time information derived from the sensors, city planners can better allocate resources, optimize traffic and plan for emergencies. For example, if fire fighters were responding to a burning building in a smart city, their route to the building would be optimized, as data would be able to be collected on all the cars and people currently on the roads. They were also have access to the 3D model of the building, and would be able to know where the location of the people in the building as well as predict the fire’s behaviour in the building. Smart cities and digital twins allow for greater innovation and creativity in urban planning, with open data and the development of Application programming interfaces, cities can develop applications using the sensors to create bottom up solutions to city problems.


From the National Research Foundation (NRF), “Virtual Singapore” was created by combining digital twins and 3D virtual semantic mapping. In “Virtual Singapore”, the 3D semantic model allows for users to scan the city landscape, similar to Google’s “Satellite View”, and highlight different objects such as buildings or parking lots in order to infer data from it. Over US$2.4 billion has been set aside for this project. Uses include the ability to measure the volume of a specific building or something as granular as the amount of sunshine a specific window will observe each day. Experiments and simulations can be run with this data, in order to calculate things such as how much energy could be generated from installing solar panels on the buildings. With over 52,000 cameras, Singapore is ranked as the 2nd safest city in the world. The smart city project has also allowed for the consolidation of ride hailing services due to their smart, connected traffic solutions.

Controversies With Smart Cities

With the advancement of smart-connected cities, there are many benefits that are outlined by government officials in support of this movement; however, there has been concerns that this will lead to a decrease in privacy for citizens when in the city. Examples of modern cities implementing these features can already be seen in the UK. Since the London riots of 2011, the UK has begun the process of updating its CCTV to a higher quality feed, along with setting up more CCTV cameras. [19] A recent survey found that there are now 5.9 million cameras all across the UK, enough to have a ratio of one camera for every 11 people [20]. While there has yet to be any cases of CCTV cameras being hacked or used for ill intent, surveillance watch dogs are worried about the future advancement of this technology as it could soon lead to lip-reading technology or facial recognition [21]. The surveillance Camera Commissioner, Tony Porter, has detailed a bleak future where citizens would have to “cup their mouths” with their hands to avoid having the cameras lip-read what they are saying.

In order to help combat fears of privacy invasion, cities looking to implement “connected cities” must look to also create and develop privacy laws and regulations that will help keep governments and surveillance companies in check. Furthermore, governments should act in a transparent manner with its citizens in detailing exactly what is being recorded, where the cameras are located, where the information is being stored, and how it is being used.

Digital Twins in Healthcare

Challenges in Healthcare

A background on the challenges faced by the healthcare industry is needed to fully understand the benefits of using digital twins.

1. Healthcare is costly and comes with risk

There will always be costs associated with providing healthcare including costs for surgery, staffing, administration, and medication. Depending in the area that you reside in, healthcare may be a significant cost. Further, diagnoses and treatment come with inherent risk. Examples include the risk for undergoing surgery, and the risk of an adverse reaction from taking certain medication.

2. Complexity from rapidly advancing medical technology

The level of patient care provided is increasing as medical technology continually advances. However, as the industry may need to adapt to this complex technology, it may increase the chance of an incorrect diagnoses and unnecessary treatment.[22]

3. Disjointed information

Information asymmetry is present in the healthcare industry and arises from a lack of information sharing between parties.[22] A patient may visit several different doctors, or hospitals, but the information generated during each visit is usually not known to other parties. The lack of information sharing leads to an incomplete picture of a patient’s overall health making it harder for doctors to make a fully informed decision.

4. Inefficiencies of operations

Inefficient allocation of resources has commonly led to things such as a shortage of staff, operating rooms, and beds.[23]

5. Medical Data

The medical knowledge that we have is taken from a sample from the population and is based on the average person.[24] It is difficult to use this data to personalize healthcare for patients due to the intricacies of the structure of the human body, and the uniqueness of each person.[24] For example, the heart may be shaped in a slightly different way, or the heart’s valve may have a slightly larger diameter.

Digitization of Healthcare

The ultimate goal is to create a personalized digital twin model of a person. Picture Source[25]

The goal of using digital twins in healthcare is to bridge the gap between the physical world of people, with the virtual world of digital information.[26] This leads to the idea of a digital patient, which is essentially the result of applying digital twin technology to the human body. A digital twin of a body part can be created from measurements taken from a person over a period of time. The different body parts will eventually lead to an integrated model of the anatomy and physiology of a person. The ultimate goal is to have a lifelong personalized model with data that is updated in real time.[26] The data model is used to better understand the functions of the heart, and the unique characteristics of each individual heart. The digital twin would be each individual patient, and this would support doctors in providing a higher level of care to patients through improved decision making.[24]

Data Required for Digital Twin

A large amount of data from past, present and future is required to create a digital twin for use in healthcare. Sources of this data include: [24][26][27]

1. Medical records

2. Medication history

3. Imaging studies (e.g. CT & MRI Scans)

4. Patient provided information

5. Behavioral/lifestyle data

6. Genomic makeup

7. Physiological characteristics

Continuous coordination between technology providers, users, and caregivers will be needed to be able to integrate the data to provide a complete picture of the patient’s overall health.[27]


By creating a digital twin of a patient or a body part, the patient can be monitored and evaluated without being in the physical proximity of the care facility.[28] This leads to more efficient resource management, and cost efficiencies for both care providers and patients. Sources of cost savings may come from not needing a physical bed, reduced administrative cost from not needing to schedule and track appointments and time savings from more accurate diagnoses and treatment.[28] A higher level of patient care can be achieved because patient care does not have to start and end at a doctor’s appointment.[29] The patient can be continuously monitored, as the digital twin will provide updated information to fill in the gaps between checkups. Further, the digital twin will allow for an easier information collection process and will more effectively use the information for learning purposes.[29] Although digital twins in healthcare has not advanced far enough to create a full digital twin of a body, there are currently many applications to specific body parts. The main difference between the applications of a digital twin in health care and manufacturing is the source of the data, and how it is updated. In manufacturing, sensors are continually transmitting data through the IoT. In healthcare, the data must be collected from different sources to paint a complete picture (refer to data requirements for digital twins in healthcare).

Current Applications to Body Parts


Cardiovascular disease causes 17.7 million deaths per year, which is about one third of the deaths in the world.[26] It is evident that treating cardiovascular disease is challenging. The fact that each heart is unique, while medical data is based on population averages contributes to this challenge.[26] A patient’s heart chambers may be slightly different, or the diameter of a certain valve may be larger than the population average. It is difficult to reconstruct and analyze a patient’s heart from 2D imaging, but it is a crucial step in determining an effective treatment plan.[26] A full digital twin of a heart does not exist yet, but technology is continually advancing towards it. An example of a digital twin of a heart is the Philips Heartmodel. This digital twin model creates a 3D view of the left heart chambers of a patient’s heart and provides functions such as the ability to determine how well the heart is pumping out blood.[26] The model is created by taking prior scientific knowledge of the anatomy of the heart, which is based on the average human heart, and combining it with advanced data analytics. This is then personalized with updated scans for a patient to create a unique model.[26] To take things one step further, the digital twin can then be used in image-guided therapy. Medical images from the digital twin model are used to guide surgeons with surgical procedures.[26] For example, a surgeon may be required to replace a damaged valve in a patient’s heart. It would be challenging to navigate through the patient’s arteries with a catheter that contains a replacement valve. With the digital twin, 3D insight is provided to simplify the process of positioning the device during both surgery and the planning process. [26]

Example of how a personalized digital twin model of a heart is created from general anatomical models. Picture Source[26]
Use Case of a Digital Twin of a Heart

The Living Heart Project

The Living Heart Project is a collaborative effort that uses digital twin technology to model the cardiovascular system (CVS) for purposes such as research, treatment, and clinical trials.[28] It is currently supported by companies such as Intel, Bayer, HP and Pfizer. The CVS model simulates any defects, diseases and reflects the health status of the patient.[28] This model allows health professionals to test multiple treatment plans to compare the outcomes without taking any risk. [28]This will help improve the accuracy of a diagnosis and allows health professionals to collect data on a patient, such as if there is an adverse reaction to a specific drug. This project was applied in a study that tested the effects of the drug Quinidine on the heart, and whether it caused drug induced cardiac arrhythmia. The study determined that the digital twin was highly accurate, has the potential to accelerate the time needed for drug discovery and research, and can reduce its time to market.[28] Future uses of this may include further clinical trials for drugs, and the potential substitute of human subjects for digital twin subjects in clinical trials.

Human Brain

The digital twin will make planning for surgeries more efficient and assist with visualization. Picture Source[30]

The weakening of an artery wall, or aneurysms, can lead to immediate death if left untreated.[28] Although it is dangerous to leave untreated, it is equally risky to undergo surgery. To avoid an invasive surgery, surgeons generally use a catheter guided implant to divert blood flow from the weakened artery.[28] Determining the fit of the implant and getting the implant in place requires a lot of accuracy. Sim&Cures worked with the company ANSYS to create a digital twin model of an aneurysm, and the arterial tree of a brain.[22] This reduces the risk of a catheter guided surgery by providing insight into the patient’s health status, and aneurysm characteristics.[28] The digital twin creates a real time patient based simulation that lasts 10 to 20 seconds.[22] This helps surgeons with planning in terms of determining the size of the implant required, and the method of insertion. Having the digital twin increases the success rate of the surgery. This is highlighted in the cases where a digital twin was used for surgery. Normally, 10% of surgeries for brain aneurysms require a follow up surgery, and when a digital twin model was used, no follow up surgeries were required.[22] This demonstrates the usefulness of the digital twin both in and out of surgery.

Other Applications

A digital twin model of the upper airway of a human was created by researchers at Oklahoma State University’s computational biofluidics and biomechanics (CBB) lab. This model tracked the movement of particles of aerosol delivered by chemotherapeutic drugs. It tested and adjusted the diameter of particles in the aerosol spray, inhalation and flow rate, and the position of the aerosol spray. With the digital twin model. The researchers were able to optimize the delivery efficiency of the drug. Without the digital twin, only 20% of the drug reached the intended tumor, while the rest of the drugs damaged healthy tissue. With the digital twin, researchers found that 90% of the drug reached the intended tumor.[22] This application of digital twins can be applied to increase the effectiveness of the treatment of cancer.

Applications to the Operations of a Hospital

A digital twin can be applied to the physical hospital itself, similar to how it is being used in manufacturing. Any disruption to the day to day schedule for a hospital may cause a ripple effect, and it is important to proactively manage any issues. By creating a digital twin of the hospital, including things such as machinery, management will be able to more effectively prevent any system outages. For example, a typical MRI machine produces over 800,000 log messages that provide insight into the technical performance of the system.[24] Having a digital twin of the MRI machine will enable management to proactively monitor the services for any technical issues, and to schedule maintenance during down time.[24] By tracking and analyzing the data, the likeliness, and type of maintenance required can be predicted in advance.[24] By “fixing” things before they are broken, efficiency will increase, and more efforts can be redirected to caring for patients.

The digital twin of a device, such as an MRI, requires the combination of four components:[24]

1. Device Data

2. AI / Data analytics

3. Device knowledge, which provides meaning to the patterns identified in the data

4. Physics-based device modeling

Further benefits of the digital twins’ applications to the hospital include the ability to optimize patient care, costs and performance.[24] By more effectively controlling workflow, staff and machine utilization, the occurrence of situations such as bed, staff and operating room shortages, and bacteria contamination will be reduced.[24] Through the digital twin, hospital management is given the ability to test planned strategic changes in a way that is normally challenging in a complex and sensitive environment such as the hospital. A secure environment is provided to test the impacts of any changes risk free.[24] With the ability to plan and test specific scenarios, the healthcare industry may be quicker to adapt technological advances to improve the level of care provided to patients.

Future Applications in Healthcare

Digital twins in healthcare with VR. Picture Source[31]

1. Digital twins and virtual/augmented reality (VR/AR)

Pairing a digital twin with virtual or augmented reality creates a lifelike simulation of a patient, or a patient’s organs that can be used for educational purposes. Surgeons and students can practice complex procedures in a environment similar to what would be experienced in the real world.[26] Further, pairing both technologies will allow for better visualization during surgery. For example, a patient specific 3D model can be overlaid on a patient’s body to allow for a doctor to know exactly what is going on.[26] Using the digital twin with VR or AR allows us to better use and understand the increased volume of data that would be created.[32] Although it may not be necessary, it would be beneficial to physically see what the data is representing. In a survey conducted by HVM catapult on whether using digital twins with VR would be beneficial, 80% of engineers responded saying they found value with the technology.[32] It allows data to be translated into a physical picture to better understand what the data is dealing with.

2. Digital twins of the entire human body

Although we do not have a full digital twin of the human body yet, the technology is currently being developed. By creating computing architecture, different digital twin models of human body parts can be connected to form a whole system.[26] This creates insights for better decision making through the holistic portrait that is created for each patient.[26] Ultimately, the digital twin of the human body can be used as a diagnostic tool. The model will be continuously updated with data, and this can be used to detect any issues, and to test potential treatment plans and its effect on the body as a whole. To take it further, it can be used to prevent diseases and other issues as well. The increased amount of data received can be used to determine common characteristics of a person, such as whether one is naturally susceptible to a certain disease, or if one is physiologically or genetically superior than the average human. These unique characteristics can be taken into consideration to create an overall health plan for a patient.

3. Digital twins and 3D printing

Patient specific digital twins can be used as a model to 3D print parts such as custom prostheses and implants.[26]

Potential Issues with Digital Twins

Although there are many applications, and benefits of digital twins in healthcare, there are a set of problems that also present itself. Data collection itself will be an issue, in terms of how it is being collected, used and stored. Data collection should be transparent, and patients should be fully informed throughout the whole process. In addition to this, the security of data should be a top priority, and not an afterthought. Digital twins, and the data that it provides, may be susceptible to hacking and viruses. Proper measures need to be in place to prevent this. Finally, and as mentioned before, digital twins create a massive amount of data and the usage of this technology involves the movement and storage of the data. There are many variables with the processes, and even a few errors could cost the life of a patient.[22] It is both about the data that is created, and how it is used. The healthcare industry has always been slow to adapt to technological innovation given the environment that healthcare professionals must work in. Healthcare professionals need to adapt to this new technology and put it to use by finding a way to use it to create actionable insights to improve the level of care provided to patients. [22]


Several ethical issues arise when it comes to using digital twins in healthcare, and the implications that it may have on the individual and societal level.

1. Equality of capabilities

Equality in terms of the ability for people to determine their own fate.[33] A digital twin may tell you how to live your life based off the data that is being processed. Having the digital twin representation may result in discrimination of the digital twin model, which may lead to discrimination of the person.[33] This may influence what you perceive you should or shouldn’t be doing in your life, and ultimately changes how you lead your life.

2. Privacy

The amount of information required to create a digital twin calls in to question the ability for people to decide how much information to give up, and how to protect this information. Personalized medicine comes with a risk of difference in the information about the risk that a person is taking, and the differences in the contractual position in the usage of data.[33] There will be some people who may inadvertently give up more data than desired. With information on the most personal level, will there be cause for concern even in a society where we are giving up information at every touch point with the internet? There needs to be consideration of how people will be protected with the implementation of this technology, and what happens when they aren’t.

3. Social issues

From the information provided, a digital twin provides an objective view on the health status of a person. It may indicate that someone is naturally “weak”, or short-lived, and may be self-fulfilling.[33] Having the institutions, and society believe in this opinion may make it more likely for you to believe it is true and cause it to happen. The social aspect provides a new layer of issues in the implementation of digital twins in health. There must be consideration of how much an individual should know about their health status. The benefits of having a personalized digital twin may be offset by the potential negative implications of a person believing they are sick or weak.

4. Distributive justice

Personalized medicine may increase the cost of healthcare at an individual level. How will we ensure that everyone is getting the same basic level of care with a digital twin, or should everyone have a digital twin at all? This will come down to whether the technology is market-driven and for profits, or if it is for society.[33] A method needs to be implemented to distribute to those not as well off. With the increased amount of information on the differences in the constitution and capabilities of people, it may come down to deciding which diseases to focus on and treat, and which diseases to write off as bad luck in the natural lottery.[33]

5. Issues with enhancement

Personalized digital twins will make it easier to target specific enhancements and may lead to different classes of people and be disruptive for society.[33] Enhancements itself may be thought of to equalize the differences between people. It has the potential to redefine the activities that we accept and consider normal and may lead to new rules and benchmark for our achievements.[33] Whether this have a positive effect or increase discrimination and be counterproductive has yet to be seen.

Growth of Digital Twins

The global digital twins’ market is projected to grow to $15.6 billion by 2023.[28]

Digital twin applications in patient care and personalized monitoring is expected to generate $152.6 million in global annual revenues (up from $15 million in 2018) by 2025, with a compound annual growth rate of 33.6%.[34] The overall digital twin’s healthcare industry is projected to generate $680 million in revenue, up from 56 million in 2018.[34]

Growth of digital twins by region. Picture Source[28]


Anthony Antillon Jacky Huang Scott Quon
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada


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