Advancing Healthcare Through the Integration of Digital Twins Technology: Personalized Medicine’s Next Frontier

Abstract

The use of Digital Twin technology has been expanding rapidly, and projections indicate that it will continue to proliferate across various applications, use cases, and industries, including healthcare. In recent years, the healthcare sector has seen an acceleration in digital transformation. This fast-paced change offers both opportunities and risks, especially with emerging technologies like digital twins, which are relatively untested and still in the early stages of adoption in medical care. This paper aims to conduct a literature review to explore how digital twins facilitate intelligent automation in healthcare. It defines the concept, traces the technology’s evolution and development, reviews its key enabling technologies, and examines current trends and challenges. The paper also presents a range of application examples in personalized medicine and public health, concluding with a succinct discussion of the primary technical and ancillary challenges, as well as the ethical issues that arise when applying digital twin technology to human subjects.

1. Introduction

Data are consistently generated throughout the healthcare system through various business activities and service provisions. These data are vital for clinical, operational, and individual decision-making [1]. However, healthcare data often remain inaccessible, lack standardization across different systems, and are difficult to understand, utilize, and share. While most healthcare organizations recognize the value of these data, many have not treated it as a crucial, tangible asset. Effective management of these data can empower healthcare systems to construct comprehensive patient profiles, elevate the quality of care and treatments, enhance communication, and ultimately improve health outcomes [2].

Efforts to enhance health data interoperability and standardization are ongoing, with technologies such as artificial intelligence (AI) and blockchain playing pivotal roles in addressing these issues. Recent studies have shown that AI can significantly improve the analysis and interpretation of health data, leading to more precise diagnoses, tailored treatment plans, and efficient resource allocation [3]. Blockchain technology is also seeing increased adoption in healthcare for secure data sharing, traceability, and accessibility. This decentralized data management approach fosters transparency, minimizes the risk of data breaches, and aids in standardizing health data across multiple platforms [4]. Additionally, natural language processing (NLP), a subset of AI, is gaining attention for its ability to extract meaningful insights from unstructured health records, enhance data organization, and improve communication among various healthcare systems [5].

The healthcare industry is one of the fastest-evolving sectors in the world, driven by a combination of technological advancements, shifting patient expectations, and regulatory reforms. The sector faces numerous challenges, including escalating costs, the transition to value-based care delivery models, payment reforms, data security concerns, and the integration of smart healthcare technologies [6]. Healthcare organizations have been rapidly accelerating their digital transformations in response to these challenges. According to a 2021 survey of healthcare executives, 81% indicated that their organization’s digital transformation is gaining speed, while 93% reported that their organizations are focusing on innovation with greater urgency this year [7]. Central to this transformation is the effective utilization of both internal and external data. Consequently, healthcare organizations are increasingly recognizing data as a valuable asset and are willing to invest in strategies to leverage them for accelerated transformation.

Digital Twin (DT) technology is one emerging concept that healthcare organizations are exploring. This technology digitally models physical environments, potentially providing real-time analytics that can aid in data-driven decision-making, risk assessment, and operational improvements. As healthcare faces unprecedented challenges, DT technology can play a critical role in centering patient care within these complex systems.

The potential of DT technology is garnering interest from both industry practitioners and academic researchers. Currently used in various sectors to create accurate digital replicas of objects and simulate operational processes, DT technology is projected to gain even more traction. Gartner predicts that by 2027, over 40% of large global companies will implement DT technology in projects aimed at increasing revenue [8]. Specifically, within healthcare, Gartner forecasts that by 2025, a quarter of healthcare delivery organizations will incorporate DT initiatives into their broader digital transformation strategies [8]. Additionally, a survey by Accenture revealed that in 2021, 25% of healthcare executives reported their organizations were already experimenting with DT technology. In reality, a significant 87% of healthcare executives believe that DTs are crucial for their organization’s ability to engage in strategic ecosystem partnerships. The same survey indicated that 66% of healthcare executives expect their investment in DT technology to increase over the next three years [9].

In reality, a significant 87% of healthcare executives believe that digital twins are crucial for their organization’s ability to engage in strategic ecosystem partnerships. Additionally, 66% of these executives anticipate an increase in their organization’s investment in intelligent digital twins over the next three years.

Although the process of transforming healthcare operations and patient experiences through Digital Twin (DT) technology has commenced, there are still only a few documented cases that highlight the business benefits gained by pioneering healthcare organizations from using this emerging technology. While still largely conceptual, DT is gradually making inroads into healthcare, aiming to fulfill unmet needs in the sector. Current research on the intersection of DT and healthcare remains limited. As DT applications become increasingly mainstream, it is crucial to investigate the challenges, benefits, and drawbacks associated with employing this technology in healthcare settings. Section 2 of this paper presents foundational perspectives and definitions of Digital Twin (DT) technology as identified in the literature. Section 3 examines four critical enabling technologies that support DT development and implementation. Section 4 investigates current trends, applications, and use cases of DT in the healthcare sector. Section 5 analyzes the challenges and opportunities associated with DT adoption. Section 6 provides a summary of the key findings and draws conclusions. Section 7 discusses the practical implications of DT for industry and research. Section 8 outlines future directions and emerging prospects for DT. Finally, final section concludes with a comprehensive reference list.

2. Origin and Concept of DT

One of the earliest notable applications of Digital Twin (DT) technology dates back to 1970, when NASA engineers used simulators as virtual twins for the command module and its electrical system to rescue the Apollo 13 mission. This life-saving intervention was executed in less than two hours, demonstrating the enormous potential of the technology at that early stage [10]. Today, NASA continues to employ DT technology in the development of next-generation vehicles and aircraft.

The term ‘Digital Twin’ was first introduced in 2003 by Professor Grieves at the University of Michigan during a course on product lifecycle management [11]. The concept has since evolved significantly, with various scholars offering different definitions and developmental frameworks [12,13,14]. According to the Encyclopedia of Production Engineering, a Digital Twin is ‘a representation of an active unique product, which can be a real device, object, machine, service, intangible asset, or a system consisting of a product and its related services’ [15]. At its core, DTs are virtual models of physical objects or systems that can be analyzed and manipulated using real-time data [16].

Although the concept of DT has been around for a while, its transition from theory to practical application has accelerated in recent years. It is anticipated that DT technology will integrate further with other technologies like voice recognition, augmented reality, the Internet of Things (IoT), and artificial intelligence (AI). Market research by MarketsandMarkets forecasts a meteoric rise for DT technology, projecting its market value to grow from $6.9 billion in 2022 to an astounding $73.5 billion by 2027—a Compound Annual Growth Rate (CAGR) exceeding 60% [17]. This surge is primarily attributed to growing interest in the manufacturing sector, aimed at cost reduction and supply chain optimization.

3. DT Technologies

Digital twin (DT) technology comprises three core components: data acquisition, data modeling, and data application [18]. DT relies on four key technologies to gather and store real-time data, generate valuable insights, and create a virtual model of a physical object. These technologies include the Internet of Things (IoT), artificial intelligence (AI), Extended Reality (XR), and cloud computing (Figure 1). The extent to which each technology is utilized varies depending on the specific application of the digital twin.

3.1. Internet of Things (IoT)

IoT, or the Internet of Things, refers to a vast network of interconnected “things”. These connections can be between objects, between people and objects, or between people themselves [19]. IoT devices and sensors serve multiple purposes, including collecting real-time data from physical assets and ensuring DTs receive up-to-date information from their real-world counterparts. By 2027, it is expected that over 90 percent of all IoT platforms will incorporate digital twin capabilities [17]. IoT acts as the foundational layer for DTs, providing the crucial data that fuel their operation. Sensors embedded in physical objects within the real world continuously gather data on various parameters like temperature, pressure, vibration, and location. These data are then transmitted to a central platform where they are processed and stored [20,21]. These collected data become the lifeblood of DTs, which are digital replicas of physical objects or systems. These DTs accurately reflect the real-time state and behavior of their physical counterparts, enabling their analysis, manipulation, and optimization in a virtual environment [22,23]. The synergy between IoT and DTs lies in their combined ability to generate actionable insights. By continuously collecting and analyzing data from connected devices, DTs provide valuable insights into the performance, health, and potential issues within the physical system.

IoT technology also enables remote monitoring and control of DTs, facilitating asset management from any location. Additionally, IoT devices and DTs can be integrated with other systems like enterprise resource planning (ERP) to streamline processes and improve decision-making.

As the Internet of Things (IoT) continues its rapid growth and evolution, the applications of digital twin (DT) technology are expected to become increasingly diverse and sophisticated. This synergistic combination is already revolutionizing numerous industries, including manufacturing, transportation, energy, and healthcare, by enabling real-time monitoring, optimization, prediction, and prevention of potential issues [20,21,22,24].

As IoT continues to evolve and expand, the applications of DT technology will become increasingly diverse and sophisticated. Industries such as manufacturing, transportation, energy, and healthcare are already benefiting from the integration of IoT and DT. These technologies enable businesses to monitor and optimize their operations in real time, as well as predict and prevent potential issues. In healthcare, DTs can be used for patient monitoring, personalized medicine, and even the development of virtual models for surgical planning or medical device design.

As IoT and digital twin (DT) technology gain traction across various industries, novel applications and use cases will continue to surface. This progression will foster innovation and generate new opportunities for both businesses and consumers. By harnessing IoT’s capabilities, DT technology holds the promise of transforming our understanding and interaction with the physical world, paving the way for a future that is more efficient, sustainable, and interconnected.

The integration of IoT and DTs has the potential to usher in a new era of connected intelligence. By leveraging real-time data and virtual models, businesses can achieve unprecedented levels of efficiency, sustainability, and resilience. As these technologies continue to evolve and new applications emerge, the future holds immense promise for a more connected and intelligent world.

3.2. Cloud Computing

Cloud computing is a technology that enables the delivery of hosted services via the internet, providing an efficient means of storing, managing, and accessing data [19]. This modern approach to computing offers various benefits, such as data computing and cloud data storage technology, which are particularly useful for applications like Digital Twin (DT).

DT, known for its ability to handle substantial volumes of data, greatly benefits from cloud computing. The integration of cloud computing with DT technology allows for the storage of massive amounts of data in a virtual cloud environment. This not only leads to a significant reduction in computation time but also facilitates easy access to crucial information from any location [25].

Beyond just processing vast volumes of data generated by the Internet of Things (IoT), cloud computing enables real-time analytics and new knowledge discovery. This computational power enhances decision-making processes and allows for immediate action based on the data insights. By leveraging cloud computing, DT can effortlessly store, process, and analyze data, making the technology more efficient, scalable, and versatile. This seamless integration between DT and cloud computing paves the way for improved data management, increased reliability, and better overall performance. As a result, organizations can optimize their operations and make more informed decisions thanks to the powerful combination of these two groundbreaking technologies [26].

Liu et al. explore the promising advantages of implementing Digital Twin (DT) concepts in healthcare, presenting a case study on CloudDTH, a cloud-based framework designed for the healthcare sector. This framework is particularly geared toward aiding the self-management of elderly individuals. CloudDTH not only provides real-time monitoring capabilities but also facilitates personalized healthcare interventions [27]. In summary, cloud-based DTs have immense potential to support real-time monitoring and personalized healthcare, making them invaluable tools in modern healthcare systems.

3.3. Artificial Intelligence (AI)

Artificial intelligence (AI) aims to understand and develop intelligent machines capable of simulating aspects of human cognition and behavior through computational systems and algorithms [28].

The study of AI encompasses various domains, such as Robotics, Image Recognition, and Language Recognition, which contribute to the advancement of this field. Neural Networks, Machine Learning, Deep Learning, and Expert Systems are some of the core techniques employed in AI to achieve these objectives. By leveraging these methods, AI systems can learn, adapt, and improve their decision-making capabilities over time, allowing them to tackle complex tasks with increasing efficiency and accuracy.

One of the practical applications of AI is in the realm of DTs. AI can be utilized to enhance DT solutions by offering advanced analytical tools that can automatically process and analyze the collected data, providing valuable insights and predictions about the system’s behavior [29]. By integrating AI with DTs, it is possible to make informed predictions about potential outcomes, as well as to devise strategies to mitigate potential problems [13]. According to [18], the combination of AI and DTs can significantly improve the overall efficiency and effectiveness of system monitoring and optimization, making it a powerful tool in various industries and applications. By incorporating AI techniques such as Neural Networks, Machine Learning, Deep Learning, and Expert Systems, the field continues to advance and find new applications, such as enhancing DTs with advanced analytics capabilities. These developments allow for more accurate predictions, proactive problem-solving, and better overall system performance [18].

3.4. Extended Reality (XR)

Extended Reality (XR) includes a wide array of immersive technologies, such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). These innovative technologies have the capability to merge the physical and digital worlds, enhancing and broadening our perception and experience of reality [30]. XR enables the creation of digital representations or simulations of objects, which then coexist alongside real-world entities, facilitating interaction between them in real time. This seamless integration of digital and physical elements paves the way for highly immersive experiences and interactive environments [31].

DT leverages the capabilities of XR to create accurate digital replicas of physical objects, processes, or systems. These virtual models allow users to engage with digital content and explore various scenarios, leading to improved understanding and decision-making. By combining the power of XR with DTs, users can gain valuable insights, test hypotheses, and optimize processes within a highly interactive virtual space. This fusion of technologies ultimately fosters innovation and paves the way for a more interconnected and efficient future [31].

As Extended Reality (XR) technologies continue to evolve, their integration with DT technology unlocks numerous possibilities for various industries and applications. This powerful combination offers a unique opportunity to simulate real-world situations, analyze data, and optimize processes while minimizing risks and costs [32]. Healthcare can greatly benefit from the synergy between XR and DT. Medical professionals can use these technologies for immersive training simulations, remote consultations, and even pre-operative planning, which can lead to improved patient outcomes and reduced medical errors.

4. DT Use Cases and Application in Healthcare

Currently, digital twin (DT) technology is primarily utilized in engineering and manufacturing to create precise virtual models of objects and to simulate operational processes. Various DT applications have been explored in operations and supply chain management, including operational traceability, transport maintenance, remote assistance, asset visualization, and design customization, as documented in recent studies [33,34,35,36,37,38]. The technology is expected to realize its potential in numerous other sectors, such as automotive, aerospace, construction, agriculture, mining, utilities, retail, healthcare, military, natural resources, and public safety [13,15,27,36,39,40,41,42,43].

DTs have achieved notable success across various industries and are now gaining prominence in healthcare. The COVID-19 pandemic acted as a catalyst for the healthcare and life sciences industry, accelerating its digital transformation initiatives [44]. During the pandemic, challenges such as disruptions in the supply chain and limitations in facility capacities necessitated the rapid adoption of DT. These digital twins enable the monitoring, simulation, and streamlining of data from diverse devices. This crisis highlighted the need for more efficient healthcare processes to cope with the massive shifts caused by the pandemic. Furthermore, healthcare providers are facing increasing pressure to digitally transform and meet growing patient expectations. Consequently, there is a rising interest in leveraging technologies like DT within the life sciences sector to enhance operational efficiency and patient care [17].

Data from sources like Electronic Health Records (EHRs), disease registries, and other systems can be integrated with information from wearables and personal health devices. By combining these extensive datasets, we can create precise profiles for individuals. This allows for simulations to forecast the impact of different medications, the development of tailored care plans, and early detection of an individual’s vulnerability to illnesses or other conditions.

The various associated data and services and concerns of human DTs are represented in Figure 2. DT solutions connect massive digital data networks across their physical assets and products to create living models of facilities, supply chains, and medical products—even body parts and organs [45]. By combining these extensive datasets, we can create precise profiles for individuals. This allows for simulations to forecast the impact of different medications, the development of tailored care plans, and early detection of an individual’s vulnerability to illnesses or other conditions. The digital model will allow healthcare providers to combine data and intelligence, ask and answer big-picture questions critical to their survival, and reimagine how they operate, collaborate, and innovate. Additionally, digital twin simulations can also help in the planning stages by providing a platform for interdisciplinary teams of healthcare professionals to collaborate. Surgeons, anesthesiologists, and nurses can run through the simulated procedure, offering their expertise on how to optimize each step. This fosters a more holistic approach to patient care, ensuring that every medical decision is rigorously evaluated from multiple perspectives before being carried out [46].

Different forms of digital twins exist in current practice, but they all fundamentally comprise three main components: the tangible object in the physical realm, its digital representation in a virtual space, and the bi-directional flow of data and information between the two. A digital thread functions as the chronological data pipeline for a digital twin, allowing for continuous tracking and tracing over a specific time span. In healthcare, this could be from the inception to the end of a lifecycle for an individual, a cell, or even a designated time period for a particular population. This time-sensitive data flow enables in-depth querying and the identification of meaningful correlations among diverse data points over the course of time [47].

Digital twins are under investigation for their potential to develop dynamic human models and simulations, aiming to enhance medical diagnostics, prognosis, treatments, and, ultimately, patient well-being [48]. Various forms of digital twins exist, ranging from models of entire human bodies to specific systems, organs, or even cellular and molecular components [49]. They can focus on particular diseases or conditions and even include interactions with external organisms like viruses. Composite digital twins combine multiple types and serve as more complex, individualized models. Reference twins act as foundational templates for creating these advanced, personalized digital twins. Healthcare entities like hospitals can also utilize digital twins of organizations (DTOs), often composite in nature, to enhance planning, monitoring, and optimization of their operations [50].

The medical sector, renowned for its trailblazing innovations, stands on the cusp of a significant transformation with the emergence of medical digital twins (MDTs). These virtual replicas, representing individual patients, are in the early stages of development but carry the promise of radically changing the healthcare landscape. MDTs offer a unique, personalized perspective into the intricate workings of human health and the nature of various diseases. Their potential lies in enabling a deeper understanding of individual patient health, allowing for more tailored and effective treatments. By accurately simulating the physical and biological characteristics of patients, MDTs can aid in diagnosing, predicting, and treating illnesses with unprecedented precision, paving the way for a new era in personalized healthcare [51].

Digital Twin technology is gaining traction in the field of life sciences, particularly in drug discovery and development, as noted in a 2021 report by KPMG. A notable example is the development of a Digital Twin of the human heart by the software company Dassault. Their technology transforms a two-dimensional scan of a human heart into a full-dimensional, accurate model, known as the “Living Heart”. This model realistically simulates the heart’s functions, including blood flow, mechanical movements, and electrical activity. Currently, the Living Heart model is being utilized globally, offering innovative approaches to designing and testing new medical devices and drug treatments. This application of Digital Twin technology represents a significant advancement in medical research and development [52].

Digital Twin (DT) solutions are increasingly being used in healthcare to construct digital replicas of patients, medical facilities, and devices. The goals of these applications are multifaceted: they include monitoring and analyzing patient care on a personalized level, conducting predictive maintenance of healthcare infrastructure, and managing rising research and development costs [53].

The advantages of employing digital twins in healthcare are vast and yet to be completely realized. For instance, Table 1 in the document highlights several use cases within the healthcare industry, demonstrating the diverse benefits of DT technology. These examples range from improving patient outcomes through personalized treatment plans to optimizing the operation and maintenance of medical facilities. Furthermore, the remaining part of this section elaborates on some of the latest applications of DT in healthcare. This includes, but is not limited to, the development of patient-specific treatment protocols, the enhancement of medical training through realistic simulations, and the improvement of medical device design and testing. By leveraging DT technology, the healthcare industry can achieve higher levels of efficiency, accuracy, and patient satisfaction.

4.1. Drug and Vaccine Development

The application of technology in drug development, particularly in testing new drugs, offers significant potential in ensuring drug safety and effectiveness. Each stage of this process generates a substantial volume of data, which presents a considerable challenge in terms of data management. Digital twins (DTs) adeptly utilize this data to construct comprehensive models of the drug development process [54]. As a result, DT can notably expedite the clinical trial phase in pharmaceutical research. This acceleration is achieved by enabling trials to proceed more rapidly and efficiently, reducing the number of patients required to undergo drug testing [55].

Expanding on this, DT’s role in the pharmaceutical industry is not limited to just enhancing clinical trials. It also extends to the pivotal task of developing and producing new vaccines. By leveraging DT, scientists can more effectively select the most promising antigens for vaccine development. This aspect of DT allows for a significant portion of the development process to be conducted virtually, streamlining the research and reducing the time and resources traditionally needed for vaccine development. Such advancements in DT technology not only contribute to faster and more efficient drug and vaccine development but also promise to revolutionize the way we approach medical research and pharmaceutical innovation, leading to safer and more effective treatments being brought to market more swiftly.

4.2. Advanced Diagnosis and Preventive Treatment

DT technology holds the potential to revolutionize patient care by simulating a wide range of patient-specific characteristics and replicating their responses in various medical scenarios. This advanced simulation capability can be instrumental in tracking patient health, facilitating more accurate disease diagnosis, and planning effective preventive treatments. By creating virtual models that mirror individual patient conditions and responses, healthcare providers can tailor treatments and interventions more precisely to each patient’s unique needs.

Additionally, the growing accessibility of health data from wearables and various medical data sources has the potential to expand precision medicine to a wider patient population. This can be achieved by offering tailored therapies that enhance efficacy and efficiency while also reducing costs for the healthcare system [56].

Moreover, the insights gained from these simulations extend beyond individual care. They can offer a representative overview of how a drug might affect a broader population. As discussed in Section 4.1, this aspect of DT technology is particularly valuable in pharmaceutical research and development. It allows for the assessment of a drug’s efficacy and safety across diverse patient demographics and conditions, leading to more effective and safer medications. This approach can significantly enhance the precision of clinical trials and the development of personalized medicine strategies.

Researchers and industry experts, as highlighted in works by [39,54,55], underscore the potential of DT technology in providing a more holistic and accurate understanding of medical treatments and their impacts. This technological advancement is poised to bring about a significant shift in how healthcare professionals approach patient care, drug development, and the management of health outcomes on both individual and population levels.

4.3. Clinical Research

DT technology is poised to transform the landscape of clinical research, offering innovative methods in medical education, research, and patient care. This technology enables the modeling of future scenarios, such as detailed surgical simulations, which can aid in both research and educational contexts. One of the most significant advantages of DT is its ability to forecast the effects of experimental treatments on patients, providing more accurate answers and actionable insights without exposing patients to risk.

Additionally, DT technology facilitates the analysis of data from infected patients, enhancing the ability to conduct treatment simulations. This capability is crucial for identifying the most promising avenues for real-world research and clinical trials [44]. A notable example of this application is from a Swedish University, which developed a DT of mice with rheumatoid arthritis. This model was used to assess drug efficacy, potentially serving as an alternative to traditional clinical trials involving human subjects [53].

Furthermore, the application of DTs in simulating invasive clinical procedures marks a significant advancement in medical practice. It enables predictive analysis of therapy outcomes, ranging from optimizing the selection of medical devices to fine-tuning surgical procedures. This leads to improved safety and effectiveness in healthcare delivery. By providing a virtual yet realistic environment for testing and analysis, DT technology allows healthcare professionals to refine their approaches and decisions in a risk-free setting. As outlined by [57], the integration of Digital Twin solutions in healthcare represents a monumental shift toward more informed, precise, and patient-centric medical practices.

4.4. Personalized Medicine

Personalized medicine technologies aim to expedite drug development processes and reduce the reliance on test subject animals. The effectiveness of medications varies significantly among patients, with failure rates ranging from 38% to 75% for various conditions, from depression to cancer [58]. This inconsistency stems from the heterogeneous nature of patients who are administered the same or similar drugs based on treatments optimized for the average individual. Efforts are also underway to identify and minimize drug side effects early on [47].

For example, some patients may have allergic reactions to specific drug components, which could lead to severe health problems. These reactions can be anticipated and mitigated by examining chemical properties in patients’ DT before administering potentially allergenic medications. In addition, in cases where allergies are not detectable through blood tests, real-time data from the patient’s DT can be used to monitor their response to drugs in a controlled manner, allowing doctors to identify and address complications early on [57]. Digital twin solutions offer a pathway to realize the global vision of personalized medicine [57].

They also enable research into complex diseases like Multiple Sclerosis and Alzheimer’s, improving treatment insights and shortening trial durations. A DT can be customized for each individual’s anatomy, physiological traits, and medical history [59]. Additionally, DT technology can simulate novel treatments, allowing for data-driven decisions that enhance patient outcomes, reduce risks, and speed up the delivery of groundbreaking therapies. For instance, researchers at Oklahoma State University developed a DT model of the respiratory system to simulate aerosol drug delivery for enhanced lung cancer treatment [53]. Another study proposed using health digital twins to address cardiovascular diseases [60]. Similarly, Siemens created a heart DT using extensive imaging data to gain a deeper understanding of heart health and predict potential issues. In France, a company developed a DT of aneurysms and nearby blood vessels to help surgeons select the most suitable tools based on DT insights [53].

Finally, one notable institution focused on personalized medicine is Linköping University in Sweden, which has made strides by creating a digital twin that maps mouse RNA to forecast medication outcomes [59].

4.5. The Rise of Medical Digital Twins

Medical digital twins (MDTs), though still in their early stages of development, represent a significant advancement in healthcare technology. As virtual representations of individual patients, they offer remarkable potential to transform the healthcare sector. MDTs provide a personalized view into the intricate aspects of human health and disease, promising to revolutionize patient care and treatment [51]. The technology enables physicians to leverage multiple data points and clinical parameters through advanced modeling to determine optimal treatment strategies [61].

MDTs enable the analysis of an individual’s unique reactions to therapies. This allows for the customization of treatment timings and dosages, enhancing effectiveness while reducing side effects [51]. Additionally, MDTs can simulate the impact of new medications or therapies on a virtual patient model. This approach is beneficial for personalized drug development and minimizes the risks typically associated with clinical trials [62]. Moreover, the extensive data from MDTs can be leveraged by researchers to identify new biomarkers specific to diseases or to predict disease progression in real time. MDTs can model the complex interactions between drugs and biological systems through knowledge graphs and AI-driven approaches. This modeling aids researchers and clinicians in understanding and predicting drug interactions and mechanisms of action for both existing and prospective treatments [63].

In another example, the Swedish Digital Twin Consortium has developed a personalized medication strategy utilizing Digital Twin Patients (DTPs). This approach involves creating a digital twin of a patient, replicating it, and testing each replica with various drugs. The goal is to identify the most effective drug for use in the patient’s actual treatment [48].

Laubenbacher et al. describe a high-level outline of a roadmap for building a prototype of an immune digital twin (IDT), as depicted in Figure 3 [61]. The intricate and multifaceted nature of the immune system plays a fundamental role in human health and disease. By capturing its key features through digital twins, we have the potential to revolutionize personalized medicine, particularly in predicting individual responses to infection and injury. This could save countless lives and dramatically improve healthcare outcomes. The roadmap emphasizes the crucial need for collaboration between the international non-profit scientific community, the commercial sector, and government funding agencies. Such an endeavor necessitates the development of a collaborative infrastructure and a unified vision for the ultimate IDT product [61].

4.6. Precision Public Health

Public health primarily focuses on interpersonal interactions, the chronic medical conditions of community members, and determinants of health at a population level. This is due to its overarching goal of promoting community wellness while also monitoring, controlling, and mitigating disease outbreaks and epidemics. Digital twins offer a vital complementary tool and open up significant research avenues in the realm of precision public health, a need highlighted by the experiences of the COVID-19 pandemic. One essential research opportunity lies in creating a virtual framework for monitoring and handling disease outbreaks, thereby equipping the public with enhanced preparedness and response capabilities [47].

A recent study introduced such a system, conceived as an integral part of a smart city, drawing lessons from China’s handling of the COVID-19 pandemic [64]. Their multifaceted model incorporates a patient database sourced from diverse inputs, cloud computing infrastructures, and artificial-intelligence-based location technologies. These elements collaborate to enable an effective response to outbreaks. The system offers traceability of diseases through analyzing proximity data in the patient database while also swiftly pinpointing individuals who have been in close contact with affected persons. Additionally, the system utilizes geolocation data to assess and categorize the risk levels across different regions. High-risk zones for disease transmission are identified and mapped, facilitating real-time alerts for visitors to these areas [64].

4.7. Facility and Operations Design

The COVID-19 pandemic highlighted the critical role of accurate facility and operational design in safeguarding public health. Digital twin (DT) technology can address issues such as inaccurate predictions of bed occupancy by integrating internal and external data sources. It enables real-time tracking of patient flow within facilities and can anticipate surges in demand based on external factors. DT empowers healthcare providers to adopt a more strategic approach to capacity and resource planning, improving cost management and enhancing patient experiences. For instance, DT can streamline operations by optimizing scheduling and identifying workflow inefficiencies. Additionally, DT solutions assist healthcare organizations in fine-tuning hospital layouts, capacity planning, workflows, staffing, and care delivery systems. By combining predictive analytics with comprehensive data tracking, DT technology offers valuable insights that enhance patient care, operational efficiency, and financial outcomes [65].

4.8. Capacity Planning

During COVID-19, the demand for hospitals surged, highlighting capacity and resource issues such as the availability of ventilators, intensive care units, and healthcare personnel. DTs of hospitals or departments can offer a comprehensive resource management and planning view, enabling more targeted activities based on needs. In DTs, machine learning and artificial intelligence techniques facilitate predictive solutions to optimize processes and resource utilization. Insights gained from these predictions can be tested on the hospital or department’s DT, with the resulting scenarios being examined by authorized experts. As a result, appropriate methods can be applied in the hospital or department, leading to more efficient resource management processes.

Siemens Healthineers used DT solutions to enhance hospital procedures in Mater Private Hospitals’ radiology department in Ireland, responding to high patient demand, extended waiting times, and other issues. A DT of the radiology department was populated with actual data, various predictions were generated, and multiple scenarios were tested. Informed by the insights from the DT, adjustments to the planning and organization of the hospital’s radiology department were identified. DT solutions are anticipated for numerous hospital systems and resource management applications, like the one in this study. They could be beneficial for addressing healthcare resource shortages during events such as the COVID-19 pandemic [57].

4.9. Medical Devices

Healthcare organizations rely on a wide range of critical assets, including monitoring and imaging devices, respirators, and surgical tools, which must always be ready for patient care. Digital twin (DT) technology leverages data from IoT sensors embedded in these devices to monitor their configuration and maintenance history. DT solutions offer real-time insights into device performance, utilization, and maintenance needs. Furthermore, DTs enable the development of customized and intelligent medical devices, optimizing both efficiency and costs. For example, DTs can precisely simulate the positioning, alignment, and dimensions of devices like stents, pacemakers, and implants. This allows healthcare providers to determine the most effective device type and positioning for each patient, thereby minimizing complications and reducing the necessity for follow-up procedures [66].

4.10. Medical Training

Virtual reality (VR) has long been utilized in medical training, enhancing traditional educational methods. This technology has supported doctors in treating patients and surgeons in performing robotic surgeries. With the integration of artificial intelligence (AI) and digital twin (DT) technology, augmented and virtual reality can now better address the training and educational needs of healthcare professionals. For instance, several companies simulate medical anatomy and surgical procedures, fostering interactive learning while reducing the reliance on cadavers [67]. Additionally, DT solutions allow medical schools to teach surgical techniques within simulated environments [39].

4.11. Diagnosis and Therapy

Researchers are leveraging digital twins (DTs) to deepen our understanding of human biology, creating DTs at the levels of single cells, genomes, or entire organs. This groundbreaking approach enables the monitoring of patient health, the diagnosis of diseases, and the planning of preventive care strategies. By testing various treatment methods on a patient’s DT, healthcare providers can determine the most effective approach, reducing both time and the costs associated with unsuccessful treatments [68]. For instance, surgeons can use a patient’s virtual replica to plan procedures, assess potential risks, or even consider alternatives to surgery [53,67]. A practical application of this technology is seen in the National Institutes of Health (NIH) DT models, which predict concussion-related trauma in athletes. This data-driven solution not only enhances surgical planning but also accelerates recovery [53].

Physiotherapists can employ DT technology to track patients’ muscle growth and monitor bone movement during physical therapy for conditions like scoliosis. Furthermore, real-time data transfer allows for studying exercise and muscle activity changes and the efficacy of various strains or exercises. Patient response to treatment and desired outcomes can be assessed by tracking their condition [57].

The innovative health digital twin concept offers a powerful tool to fight cardiovascular disease, a major global contributor to mortality [60]. Utilizing Extended Reality (XR) and digital twins (DT), medical professionals can develop customized virtual representations of patients’ cardiovascular systems, allowing for more precise and efficient analysis and optimization of treatments. This virtual platform also promotes a more profound comprehension of various treatment options’ efficacy and potential side effects, leading to well-informed decisions and improved patient outcomes. In addition, by employing advanced analytics, trends can be recognized, early warning indicators detected, and the probability of future cardiovascular events predicted. This proactive methodology empowers healthcare providers to act early, halting disease progression.

Finally, the digital health twin can be distributed to experts worldwide, facilitating remote discussions and cooperation among healthcare practitioners. This can lead to enhanced access to specialized treatment and a more holistic strategy for managing cardiovascular diseases.

5. Technical Challenges and Ethical Issues

The COVID-19 pandemic has spurred rapid growth in the digital twin (DT) market across multiple sectors, including healthcare. This expansion is further supported by declining technology costs, enhancing both IoT and DT accessibility. Over recent years, DT has found essential applications in business, with projections indicating that its use will continue to grow across various industries and applications [40]. Moreover, major cloud providers like Google Cloud and Microsoft Azure are introducing cloud-based DT platforms to enable easy access and tailored solutions. For example, in January 2022, Google Cloud launched a supply chain DT to improve operational visibility within the manufacturing sector [44].

5.1. Key Challenges and Requirements

Digital Twin (DT) technology has the potential to revolutionize healthcare by improving patient care, reducing costs, and advancing medical research and education. However, its widespread adoption faces several challenges that must be addressed for successful integration into healthcare systems. Healthcare providers can leverage DTs for strategic planning, personalized treatment, and operational improvements, provided the technology is implemented effectively with attention to its associated challenges.

5.1.1. Data Quality, Standardization, and Management

The development of a DT for healthcare requires access to social and environmental determinants and access to large-scale, high-quality healthcare data repositories, encompassing genetic information, medical records, imaging, histopathology, and other sources. These datasets must be accurate, complete, and standardized to ensure reliable digital twin models [47]. Effective data management further necessitates the use of standardized metadata, ontologies, and controlled clinical terminologies to prevent disorganized or unusable datasets, sometimes referred to as a “data swamp”.

5.1.2. Interoperability and Integration

A critical challenge in implementing DTs is achieving interoperability among diverse systems and medical devices within the Digital Health Twin (DHT) framework. The lack of standardized data representation and exchange protocols hinders seamless communication between electronic health records, medical devices, and other healthcare technologies. Standardization efforts are essential for enabling the integration of patient-specific information, thereby enhancing the accuracy and completeness of DT models [69,70]. Without universal protocols, scalability and data exchange between healthcare organizations remain challenging.

5.1.3. Technological Infrastructure

Developing DTs requires significant computational power to process and analyze extensive biological data and simulate real-time interactions. High-performance computing capabilities are essential to maintain the accuracy and predictive capabilities of DT models [69]. Furthermore, upgrading outdated IT infrastructure and ensuring robust connectivity are necessary to support the demands of DT implementation. The complex architecture of DT systems, including sensors, software platforms, and data processing tools, can pose substantial technical hurdles and necessitate significant investments in infrastructure [71].

5.1.4. Privacy and Security

Data privacy and security are critical in DT adoption, as healthcare data are highly sensitive and susceptible to breaches. Protecting individual data requires strict adherence to regulations and guidelines, along with robust cybersecurity measures [47]. Addressing these concerns is paramount to gaining trust and ensuring compliance with legal standards.

5.1.5. AI Bias, Transparency, and Reproducibility

Like AI and big data analytics, DT technology faces challenges related to bias, limited transparency, and issues with reproducibility. Ensuring that models are unbiased and explainable is essential to maintain credibility and fairness in healthcare applications. Inconsistent or incomplete data can lead to inaccurate or unreliable DT models [49,72].

5.1.6. Training and Workforce Readiness

The successful implementation of DTs requires extensive training for healthcare professionals. Staff must understand the technology’s complexities and be equipped to prioritize patient safety. Building this expertise is critical for integrating DTs into clinical practice effectively.

5.1.7. Costs and Scalability

High deployment costs represent another significant barrier to DT adoption. Implementing DT solutions requires substantial investment in technology platforms, infrastructure, maintenance, and security. These high fixed costs may deter smaller healthcare organizations from adopting the technology, limiting scalability [71].

5.2. Ethical Issues

The application of digital twin technology in personalized medicine carries the potential for groundbreaking advancements in healthcare. However, it also raises concerns about equitable access and ethical implications. For one, the technology may not be universally accessible to every individual or community, thereby exacerbating existing inequalities and creating a new form of ‘digital divide’ [47]. In regions where healthcare resources are already limited, the introduction of sophisticated digital twin systems could further marginalize those who cannot afford or access this level of personalized care [73].

Moreover, while digital twins can aggregate and analyze data across a broad population to identify medical patterns, this capability comes with ethical complexities [47]. The technology might inadvertently lead to segmentation that could be deemed discriminatory. For instance, if patterns in the data suggest higher susceptibility to certain conditions among specific ethnic or socio-economic groups, there is a risk that these groups could face stigmatization or unfair treatment. This could manifest in various forms, such as higher insurance premiums or even restricted access to certain types of medical care [73].

Therefore, while digital twin technology in personalized medicine holds immense promise, it also necessitates careful consideration of ethical and socio-economic factors to ensure that its benefits can be universally realized without introducing new forms of inequality or discrimination [74].

5.3. The Path Forward

To realize the full potential of DT technology, the following actions are crucial:

• Standardization and Collaboration: Develop universal standards for data representation and interoperability. Collaboration among healthcare organizations, big data companies, and biomedical research centers is necessary to guide model development and ensure seamless integration.

• Enhanced Data Handling Capabilities: Invest in infrastructure and processes to manage vast datasets efficiently, ensuring high data quality and integrity.

• Privacy and Security Frameworks: Strengthen regulatory and cybersecurity measures to protect patient data and build trust among stakeholders.

• Scalable Solutions: Design cost-effective and scalable DT platforms to make the technology accessible to organizations of varying sizes.

By addressing these issues, DT technology can move beyond its current limitations and unlock transformative opportunities in healthcare.

6. Summary and Conclusions

Progress in artificial intelligence (AI), the Internet of Things (IoT), and cloud computing has provided a robust foundation for the rapid evolution of digital twin (DT) solutions. These technological advancements have paved the way for DT applications to expand across numerous industries, including healthcare. With unprecedented pressure on the healthcare sector, providers can deploy DT solutions to better meet the standard of high-quality patient care. Healthcare executives expect to increase their investment in DT to improve performance, highlight areas of improvement, enable customization and personalization of medicines and diagnostics, and allow the development of new drugs and devices. Digital twin technology provides a platform for exploring innovations, preparing for various future scenarios, and testing countless “what-if” possibilities. Its potential is vast, especially in areas such as surgical training and the simulation of medical devices and treatments. For instance, when a DT models the human heart, it enables doctors and clinicians to pre-simulate the most effective therapies, potentially avoiding unnecessary procedures.

In the fields of medicine and public health, digital twin (DT) technology has the potential to transform traditional electronic health and medical records. By enhancing these systems, DTs can improve both individual records and large-scale population datasets, ushering in an era of greater precision and accuracy in healthcare.

While the adoption of DTs in healthcare is advancing, this rapid growth brings both opportunities and risks, as the technology is still in its early stages. Consequently, some healthcare providers are exploring DTs with select partners. In the near future, organizations will be able to collaborate within DT environments, sharing designs, insights, and information across systems, testing innovations virtually, and delivering care in previously unimaginable ways.

DT technology is propelling a significant digital revolution, promising unprecedented efficiencies. In the future, DT applications in healthcare will expand, incorporating other technologies like augmented reality (AR) for immersive experiences and artificial intelligence (AI) for enhanced insights and analytics. Such advancements reduce the need to verify results against physical counterparts, leading to new opportunities for DT applications in healthcare operations.

However, DT technology also presents challenges, including the risk of increasing the ‘digital divide.’ Additionally, it could result in discriminatory segmentation based on patterns identified across a population of digital twins. To address these issues, governance structures are essential to protect individual rights, ensure data privacy, and promote fairness and transparency in data use and in the equitable distribution of benefits at both individual and societal levels.

Despite the potential, implementing medical digital twins (MDTs) faces several obstacles. Chief among these are ensuring data privacy and security, addressing ethical concerns around patient ownership and control of health data, and smoothly integrating MDTs into existing healthcare systems [75,76]. These challenges require careful consideration and resolution.

Nevertheless, the potential of MDTs to revolutionize personalized medicine is undeniable. With these virtual models, healthcare can be tailored to meet each patient’s unique needs, leading to better health outcomes and a healthier future for society.

Currently, DT technology is not yet widely used in clinical settings. Although there are ongoing efforts to develop DTs representing physiological functions, individual organs, or complete models, the practical use of digital patient twins in daily medical practice remains limited [77]. There is significant potential for hospitals and laboratories to adopt this technology more extensively, which would enhance digital simulations, optimize clinical processes, and improve the quality of medical care overall.

However, there is a concern that even as the use of digital twins in healthcare grows, it may continue to be costly and not universally accessible. This could result in digital twin technology becoming a privilege available primarily to those with greater financial means, potentially leading to increased inequality within the healthcare system.

7. Practical Implications

The medical field, long known for its pioneering advancements, is now poised for another transformative leap: the rise of medical digital twins (MDTs). These digital replicas of individual patients, although in early development, promise to revolutionize healthcare by providing a customized view into the intricacies of human health and illnesses. MDTs are capable of forecasting disease progression in specific patients, enabling early diagnosis and intervention before severe symptoms arise. This personalized predictive power could significantly alter the management of various diseases, ranging from chronic conditions like diabetes to more complex ailments such as cancer.

The utility of MDTs goes beyond mere prediction. They have the potential to fine-tune treatment schedules by assessing an individual’s response to current therapies. MDTs could suggest tailored treatment plans and dosages, enhancing effectiveness and reducing adverse effects. Furthermore, MDTs can simulate the impact of potential new treatments within a virtual patient environment, fostering personalized drug development and lowering the risks linked to clinical trials. The analysis of the extensive data generated by MDTs may lead to the discovery of new disease-specific biomarkers or enable real-time tracking of disease progression. Additionally, MDTs can provide insights into the interactions between drugs and biological pathways, offering a deeper understanding of the action mechanisms of existing and prospective treatments.

MDTs can take various forms. Some are data-driven, leveraging extensive patient datasets to statistically predict disease paths. Others are based on mechanistic computational models that replicate the complex functions of the human body at a cellular level. Future MDTs might merge these approaches, offering a more complete and accurate depiction of individual health.

However, developing and implementing MDTs face several challenges, such as ensuring data privacy and security, addressing ethical issues concerning patient data ownership, and integrating MDTs into current healthcare systems.

Despite these obstacles, the benefits of MDTs are unmistakable. By leveraging these virtual models, we can enter a new phase of personalized medicine, where healthcare is customized for everyone’s specific needs, leading to better health outcomes and a brighter, healthier future for everyone.

8. Future Perspectives

DTs provide a safe environment for exploring innovations, planning for various potential futures, and experimenting with countless “what-if” scenarios without any risk. In healthcare, DT research has primarily concentrated on generating replicas of human organs, comprehending cellular behavior, and administering suitable medications and therapies. As sensors, IoT, and machine learning technologies evolve alongside imaging systems, the advancement of DT technology will gain momentum. This technological progress will facilitate significant steps toward realizing a complete human body DT, a long-standing aspiration. Moreover, DTs will be increasingly employed to address challenges in managing hospital systems and medical resources, highlighted by the COVID-19 pandemic. DT solutions are also anticipated to expedite the development of vaccines and medicines related to COVID-19.

This study examines crucial DT research in various healthcare branches, which could inspire new investigations, and explores our projections for the future growth of these technologies. It is expected that DT will replace the pre-clinical and clinical trials of animals and humans in the future. Therefore, healthcare providers should increasingly adopt DT solutions to improve patients’ quality of life, cut their research and development costs, and bring more treatments to the market. As we advance, comprehensive studies on DT technology can be conducted from various angles, such as aerospace, education, security systems, and military, in addition to the health field discussed in this study. Furthermore, research can explore more specialized application areas that have yet to be examined in the healthcare domain.

This study was limited in the scope of data collection. Future research could focus on gathering information from specific case studies to produce more comprehensive perspectives.

Author Contributions

Validation, S.A.; Investigation, S.A.; Writing—original draft, M.A.; Writing—review & editing, S.A. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. Technologies of DTs.

Figure 2. Associated data and services for human DTs.

Figure 3. A proposed immune digital twins. Source: [61].

Table 1

Potential Use Case and Benefits.

Potential Use Cases	Benefits Gained
Clinical Research	Improve clinical operations and reduce healthcare risks Enables predictive analysis of therapy outcomes
Facility & Operations Design	Help reviewing operational strategies Optimize staffing schedules Accurate bed occupancy predictions Accurate medical device utilization Help improve operational efficiency by identifying bottlenecks in workflows
Diagnosis & Preventive Treatment	Can simulate various patient characteristics and replicate how they behave Enable precision testing of thousands of treatments Test limitless “what-if” scenarios
Drug and Vaccine Development	Help test new drugs to ascertain safety and effectiveness Can speed up clinical trials in drug research Help developing and producing new vaccines
Personalized Medicine	Offer a DT created for each patient’s body, physiology, and medical history Help model the best course of treatment for a patient Can create simulations of new treatments leading to better-informed decisions
Innovative Process	Gaining big-picture visibility Help reimagine innovation processes Help strategize for many possible futures
Medical Devices	Provide real-time visibility into how medical devices are utilized, their performance, and the need for repair Help with maintenance scheduling for medical devices/equipment during downtime Can be used in the modeling of personalized and intelligent medical devices
Medical Training	Help with surgical training and treatments Help with medical education, research, and care delivery. Minimize the usage of cadavers
Diagnosis and Therapy	Help surgeons to plan the surgical procedure Identify the risks associated with the surgery Preventive treatment Pre-simulate optimal therapies Avoid unnecessary interventions
Managing Risk	Create a safe environment where you can test all possible changes and crises
The Rise of Medical Digital Twins	Enable physicians to leverage thousands of variables to intelligently model a patient’s best course of treatment Enable the analysis of an individual’s unique reactions to therapies

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