The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks

BACKGROUND A digital twin (DT) can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. Virtual models, can integrate with all the latest technologies, such as the Internet of Things (IoT), cloud computing and artificial intelligence (AI). Digital twins have applications in various sectors, ranging from manufacturing and engineering to healthcare. They have been used in managing healthcare facilities, streamlining care processes, personalizing treatments, and enhancing patient recovery. By analysing data from sensors and other sources, healthcare professionals can develop virtual models of patients, organs, and human systems, experimenting with various strategies to identify the most effective approach. This approach can lead to more targeted and efficient therapies while reducing the risk of collateral effects . Digital twin technology can also be used to generate a virtual replica of a hospital to review operational strategies, capabilities, personnel, and care models to identify areas for improvement, predict future challenges, and optimize organizational strategies. The potential impact of this tool on our society and its well-being is quite significant. OBJECTIVE The objective of the article is to present a general overview of the use of digital twins in healthcare METHODS With the aim of analysing and investigating the use of digital twins in health, a quick literature review was conducted on the topic in question using the following mesh terms: “digital twins”, “digital health”, and “health care”. Another filter applied in the search strategy was publications within a time range of up to 5 years (2018 to 2023). The search was conducted in six academic databases: IEEE Xplore, Dimensions, Scopus, Web of Science, PubMed and ACM. After applying the search strings and the exclusion criteria, a total of 58 publications were identified. The exclusion criteria described in the article were applied, resulting in 13 publications listed to constitute and support the discussion of this article. RESULTS The selected studies can be categorized according to the application of digital twins in the health sector into 2 groups: the clinical applications group, with 7 records, and the operational applications group, with 6 records. In the clinical applications group, five articles focused on the theme of personalized care/precision medicine, one related to the reproduction of biological structures and one focused on ethics issues related to the use of DTs in healthcare. In the operational applications group, we have a subgroup, with five articles that discuss the application of digital twins supporting the optimization of operational processes and another subgroup with one article that relies on the construction of virtual structures such as a hospital. CONCLUSIONS The use of digital twins, in process optimization and healthcare, presents important challenges related to data integration, privacy and interoperability. However, trends indicate exciting potential in personalizing treatment, prevention, remote monitoring, informed decision-making, and process management, which can result in significant improvements in quality and efficiency in healthcare. This work could, in some way, contribute to expanding discussions on the topic, opening space for new reflections. More in-depth future studies should be carried out to explore the possible consolidation of the use of digital twins in health, especially in processes linked to health care and primary health care, or even clarify which initiatives should be implemented or even strengthened to sustain the progress achieved thus far. CLINICALTRIAL not applicable

Challenges and Potentials of Digital Twins and Industry 4.0 in Product Design and Production for High Performance Products

• Digital Twins enhance design, operation, and maintenance of real-world assets. • Review covers Digital Twins' lifecycle applications across diverse domains. • Key sectors: manufacturing, healthcare, agriculture, aerospace, construction. • Expert panel rates Digital Twins' Technology Readiness Level at 4.8/9. • Integration with digital thread is the most significant current challenge. Digital Twins have emerged as a powerful in silico method for the design, operation, and maintenance of real-world assets across various domains. This review paper investigates different aspects of Digital Twins, especially their application throughout the product lifecycle and across diverse engineering domains. It provides a comprehensive overview of different services that Digital Twins can deliver at each stage of the product lifecycle in collaboration with other digital technologies (e.g., Internet of Things, etc.) using a data chain (digital thread). The review begins with an introduction to Digital Twins, highlighting their fundamental principles and significance in engineering. It then explores various domains to provide cross-domain insights into the use of Digital Twins. These domains include manufacturing, healthcare and medicine, agriculture and food supply chain, aerospace, construction and building management, automotive and transportation. Each domain-specific section explores recent advancements, showcasing innovative approaches and how Digital Twins improve decision-making, while also addressing their current challenges. Furthermore, each section peers into the future, presenting a forward-looking perspective on the challenges and evolving landscape of Digital Twins in that specific engineering sector. Finally, it provides a panoramic view of the current status of Digital Twins in engineering fields, based on evaluations by a panel of experts. The expert panel assessed the Technology Readiness Level (TRL) of Digital Twins at 4.8 on a scale from 1 to 9. They identified the integration of Digital Twins with the digital thread as the most significant current challenge.

'Digital twins', also called 'digital patient twins' or 'virtual human twins' - digital patient-specific models derived from multimodal health data - are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.

Meta-inventory management decisions: A theoretical model

Digital twin is an efficient technology. With digital twin evolving, various sectors can gain advantages from it. The advancement of the Internet of Things (IoT), has helped digital twin to evolve. A digital twin is identical in all aspects with its original counterpart and can be used in running simulation, predicting errors of objects, products, etc. In connection to the healthcare sector, digital twin can help in it in different ways. Just like how it can provide advantages to various sectors, namely manufacturing sector, aviation sector, etc., similarly it can provide advantages to the healthcare sector as well. Using this technology, the healthcare sector can benefit largely. In this chapter, a literature based survey is presented on the application of Digital Twin and its challenges in healthcare. It is found that there are different applications of digital twin in healthcare sector. A real life example of the use of digital twin in healthcare application is also discussed and explained briefly in this chapter. The challenges digital twin face in the healthcare are also not just briefly discussed but also explained in this chapter.

The article focuses on the project to develop a supply chain management information system using a digital twin for the maritime transport chain. Digital twins reflect real objects and processes in a virtual environment, which enables analysis, modelling and optimisation of supply chain operations. The author describes the main stages of the project, including requirements definition, system architecture development, implementation and testing. Particular attention is paid to the benefits of using digital twins in supply chain management, such as increased efficiency, reduced risk and improved forecasting. The results of the study show the potential of digital twins to optimise supply chain management and increase the competitiveness of companies. The article presents an approach to building a new model of interaction and systematic optimisation of business processes based on digital technologies at all stages of supply chain formation, which ensures the creation of a digital twin for the maritime transport chain. In order to improve supply chains, a digital twin has been proposed, which opens up great prospects for the management of logistics processes and the exchange of information between supply chain participants. This paper presents a framework for an integral Digital Supply Chain Twin (DSCT) capable of covering the entire transport chain. The proposed information system based on the use of the DSCT provides a number of improvements throughout the supply chain, as well as the ability to model and evaluate different scenarios for the supply chain. This creates opportunities to reduce the cost of organising and coordinating transport, improve the quality of transport and logistics services and make them more reliable by identifying potential problems at an early stage. The article analyses the main information technologies that facilitate the use of DSCT as a useful and reliable tool.

The integration of Digital Twin technology in small and medium‐sized enterprises (SMEs) has brought forth a transformative paradigm in engineering practices. This paper presents a comprehensive overview of the use of Digital Twin in SMEs and its implementation of advanced digital technologies in an engineering context. The paper highlights key benefits, such as the ability to simulate, analyze, and predict the behavior of products or processes before their physical instantiation. By leveraging advanced modeling and simulation techniques, SMEs can efficiently explore multiple design iterations, identify potential issues, and optimize engineering solutions without costly physical prototyping. Furthermore, the paper discusses integrating other advanced digital technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), with Digital Twin to create intelligent and interconnected systems. The implementation of Digital Twin technology in SMEs extends beyond product design to encompass various aspects of the product lifecycle, fostering efficiency, accuracy, and innovation. However, challenges such as data integration, cybersecurity, and skill requirements are addressed. With proper planning and investment, SMEs can unlock the full potential of Digital Twin technology to gain a competitive edge in the dynamic engineering domain.In conclusion, this paper demonstrates that the use of Digital Twin in SMEs and its integration with advanced digital technologies revolutionizes engineering practices. By offering virtual representations of physical assets and processes, Digital Twin enables SMEs to make informed decisions, optimize performance, and drive innovation. As the technology evolves, it becomes an indispensable tool for SMEs seeking to thrive in the ever‐evolving engineering landscape.

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations.

Given the high mortality rate from cardiovascular diseases and the need to transfer medicine to a more high-tech level of development, the authors propose the use of digital twin technology for the diagnosis and treatment of heart diseases. As a digital twin of the human heart, it is proposed to use the combination of an equivalent electric heart generator and the of D. Noble computer heart model within the subsystem for supporting medical decision-making. The use of such digital twin will make it possible to reliably conduct non-invasive cardiodiagnosis to select drugs within the treatment regimen.

The use of digital twins is becoming a foundation for automation that will improve how industries handle and organize data about both virtual and physical things. It enables analyzing industrial data more seamlessly by merging the Internet of Things (IoT) with Artificial Intelligence (AI) to make sense of it. The growth of online retailers has made it harder for logistics professionals to keep people safe, make sure products are of superior quality, and run smoothly. The most recent advancements of digital twin (DT) have made it easier to create predictive maintenance. Using DT makes it easier to accurately assess equipment status and detect problems before they occur, thereby making the system more reliable. This shift from reactive to preventive operations makes maintenance plans more efficient, reduces disruptions, and boosts the company's profits and competitive advantage. Nevertheless, the research and implementation of Digital Twin (DT) for Predictive Maintenance remains developing, likely due to the incomplete exploration of the function and significance of machine learning (ML) within this context by academics and industry alike. In this paper, a digital twin solution in which the logistics equipment is monitored and continuously maintained through the application of ML algorithms in an IOT environment is proposed. The logistics 2.0-enabled system generates virtual replicas of physical logistics assets, such as forklifts, conveyor belts, automated guided vehicles, cranes, and warehousing robotic, and the digital twins are synchronized in real-time via IoT sensor networks. For anomaly detection, Remaining Unit Life (RUL) prediction, and failure classification, this study utilized Isolation Forest (iForest), Autoencoders, Long Short-Term Memory (LSTM) networks, and Random Forest (RF) machine learning models. The architecture consists of three layers, each of which is connected to the other named the physical layer, including heterogeneous IoT sensors (vibration, temperature, acoustic, and current/voltage, GPS, and load sensors); the digital twin layer, enabling real-time synchronization and simulation; and the ML layer running predictive maintenance and optimization models. Results from the application of the proposed system show a 30-50% reduction in the equipment downtime, 20-40% diminishment in the maintenance cost, longer lifespan for the equipment, and better operational safety.

Artificial intelligence (AI) has brought about a disruptive change in the way human resource (HR) processes are being carried out. The application of AI tools in various aspects of HR activities like recruitment and selection, screening of prospective technologies, and performance appraisal has made the HR process seamless and efficient. The use of digital twin (DT) technology in human resource management (HRM) is still evolving. However, there are instances where DT technology is used in HRM for workplace planning, identification of skill gaps among the employees, and boosting productivity. In spite of various benefits of AI and DT in HRM, there are a number of ethical challenges and considerations which arise as a result of the use of such technologies. The mechanism of functioning of such technologies raises various ethical dilemmas like privacy, transparency and accountability, and biasness in recruitment. The present study, with the help of real case examples, attempts to highlight the various ethical issues and concerns with regard to the use and application of AI and DT and devises and strategizes mechanisms to address and resolve the challenges and the governance and compliance-related regulations pertaining to AI.

Biopharma Is Going Digital … Bit by Bit

Cyber-physical systems, with the advent of the Internet of Things (IoT), have paved the way for the rise of the Industrial Internet of Things (IIoT) in industrial environments. A significant manifestation of this synergy is the Digital Twin—a virtual representation of industrial entities, including robots. This paper examines the fusion of digital twins in swarm robotics, emphasizing communication, and task allocation among robots. This study employs several mobile robots interconnected via a wireless network, each equipped with a Raspberry Pi and specific sensors. A centralized server, acting as both a Docker host and a Docker registry, facilitates task allocation. The results indicate effective communication among robots, with the digital twin playing an instrumental role in safety assessment and task allocation, as well as deploying Docker containers using Portainer and GitLab APIs. Unity3D aids in visualizing IMU motion and orientation. The motivation behind this study stems from the need to enhance efficiency and safety in industrial settings through advanced automation and coordination in swarm robotics. This research not only demonstrates the feasibility of integrating digital twins with IoT-enabled robots but also sets the stage for future investigations into more complex task allocation and safety protocols in industrial environments. By leveraging digital twins in swarm robotics, we open new pathways for research into more adaptive, responsive, and efficient industrial systems.

Цифровые двойники, представляющие собой виртуальные аналоги физических объектов или процессов, становятся все более важными инструментами в современной промышленности. Данная статья исследует влияние цифровых двойников на улучшение производственных процессов и предоставляет экономическое обоснование их применения. Цель исследования состоит в анализе конкретных случаев внедрения технологий цифровых двойников на предприятиях различных отраслей промышленности и оценке их эффективности. В первой части статьи рассматриваются основные концепции и технологии, лежащие в основе создания и использования цифровых двойников. Описаны ключевые компоненты цифровых двойников и методы их интеграции в производственные цепочки. Обсуждаются преимущества, которые цифровые двойники приносят в процесс управления и оптимизации производств, включая улучшение контроля качества, уменьшение времени простоя оборудования и повышение общей эффективности операций. Вторая часть работы посвящена эмпирическим исследованиям и разбору конкретных примеров внедрения цифровых двойников в промышленности. Приведены случаи использования технологий в авиастроении, автомобильной промышленности и энергетическом секторе. Анализируется, как цифровые двойники способствовали оперативному выявлению и устранению неисправностей, оптимизации планирования и прогнозированию технического обслуживания. Важное внимание уделяется экономическим аспектам, таким как снижение затрат на обслуживание и повышение рентабельности производственных операций. Заключительная часть статьи обобщает результаты исследования, подчеркивая значительное влияние цифровых двойников на улучшение производственных процессов. Отмечены перспективы дальнейшего развития и использования технологий в контексте быстрого прогресса в области искусственного интеллекта и Интернета вещей. Экономическое обоснование применения цифровых двойников подтверждается существенно улучшенными показателями производительности и рентабельности, что делает их важным элементом стратегии развития современных предприятий. В целом, исследование демонстрирует, что цифровые двойники играют ключевую роль в трансформации промышленных процессов, способствуя повышению эффективности и экономической выгодности производства. Digital twins, which are virtual analogs of physical objects or processes, are becoming increasingly important tools in modern industry. This article explores the impact of digital twins on improving manufacturing processes and provides an economic rationale for their application. The aim of the study is to analyze specific cases of implementing digital twin technologies in enterprises across various industrial sectors and to assess their effectiveness. The first part of the article examines the main concepts and technologies underlying the creation and use of digital twins. Key components of digital twins and methods of integrating them into production chains are described. The advantages that digital twins bring to the process of management and optimization of production, including improved quality control, reduced equipment downtime, and increased overall operational efficiency, are discussed. The second part of the work is devoted to empirical research and the analysis of specific examples of digital twin implementation in industry. Cases of technology use in aerospace, automotive, and energy sectors are presented. It is analyzed how digital twins contributed to the rapid identification and elimination of faults, optimization of planning, and prediction of maintenance. Significant attention is given to economic aspects such as reducing maintenance costs and increasing the profitability of production operations. The concluding part of the article summarizes the research results, highlighting the significant impact of digital twins on improving manufacturing processes. The prospects for further development and use of technologies in the context of rapid progress in artificial intelligence and the Internet of Things are noted. The economic rationale for the application of digital twins is confirmed by significantly improved productivity and profitability indicators, making them an important element of the development strategy of modern enterprises. Overall, the study demonstrates that digital twins play a key role in transforming industrial processes, contributing to increased efficiency and economic profitability of production.