Digital twins in oncology: Revolutionising precision cancer care

Abstract: Digital twin technology, which creates virtual replicas of physical assets, processes, and systems, is transforming industries by enabling real-time monitoring, simulation, and optimization. This book chapter explores the fundamental principles, key components, and diverse applications of digital twins across various sectors, including manufacturing, healthcare, energy, automotive, and smart cities. Through detailed case studies, the chapter illustrates the successful implementation and significant benefits of digital twins, such as enhanced operational efficiency, cost reduction, improved risk management, and accelerated innovation. It also addresses the technical challenges, security and privacy concerns, and regulatory issues associated with digital twin technology. Looking forward, the chapter highlights future trends and developments, predicting advancements in AI, edge computing, 5G, and quantum computing that will further enhance digital twin capabilities. The chapter concludes with a forward-looking perspective on the transformative potential of digital twins in shaping a smarter, more connected, and sustainable world. Keywords: Digital Twin Technology,Real-Time Monitoring,Simulation and Optimization,Manufacturing Efficiency,Predictive Maintenance,Healthcare Innovation,Energy Management,Smart Cities,IoT Integration,AI and Machine Learning,Edge Computing,5G Connectivity,Quantum Computing,Operational Efficiency,Risk Management,Sustainability,Industry 4.0,Virtual Prototyping and Future Trends in Technology. Condori, P. P. C. (2022). Digital Twin in Development of Products. Digital Twin Technology, 205–218. Portico. https://doi.org/10.1002/9781119842316.ch13 Fryer, T. (2019). Digital Twin - Introduction. This is the age of The Digital Twin. Engineering & Technology, 14(1), 28–29. https://doi.org/10.1049/et.2019.0125 Gnanamalar, R. H. (2024). Human Digital Twin Processes and their Future. Transforming Industry Using Digital Twin Technology, 187–217. https://doi.org/10.1007/978-3-031-58523-4_10 Korhan, O. (2023). Introductory Chapter: Digital Twin Technology. Digital Twin Technology - Fundamentals and Applications. https://doi.org/10.5772/intechopen.113345 Mythily, M., David, B., & Vijay, J. A. (2024). Digital Twin Application in Various Sectors. Transforming Industry Using Digital Twin Technology, 219–237. https://doi.org/10.1007/978-3-031-58523-4_11 Seolin Galindo, E., & Chagas, U. (2023). Perspective Chapter: Digital Twin Applied in the Brazilian Energy Sector. Digital Twin Technology - Fundamentals and Applications. https://doi.org/10.5772/intechopen.112598

The digital twin (DT) presents an opportunity for the integration of the physical world into the digital world. DT technology has the potential to transform the construction industry and respond to some of its challenges. In conventional construction projects, progress is largely monitored by direct observation and measurement which suffers from numerous challenges, including low productivity, blunders, and poor technology advancements. Concerns are now being raised about integrating technology for autonomously monitoring building activity. In other sectors, DT technology has been responsible for saving product development time and costs by up to 50%. However, DT is still lagging the adoption of new technologies in the construction industry. The overarching aim of this study was to explore the adaptability of DT in construction site progress monitoring. This study comprehensively reviews and analyses DT concepts, technologies, and applications in the construction industry, parameters of applications of DT in construction site progress monitoring, how DT could be used for site progress monitoring in construction, common challenges in the implementation of DT in site progress monitoring, and strategies such as barriers related to DT in site progress monitoring, using literature findings while incorporating qualitative analysis of semi-structured interviews. This research shows that DT has a high potential to solve the numerous challenges in construction site progress monitoring, rather than other current technologies in use. Thus, this study raises awareness and the need for the application of DT in construction site progress monitoring

Digital Twin (DT) technology creates a dynamic virtual representation of a physical system using real-time data and computational modeling. While DTs have demonstrated profound impact in several medical disciplines, their translation into dentistry is still emerging and has not been comprehensively mapped. To systematically review and delineate the current applications, technological advancements, and prospective opportunities of digital twin (DT) technology in dentistry. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of MEDLINE (PubMed), EMBASE, Scopus, and Web of Science identified English-language publications from January 2000 to April 2025. All empirical and conceptual studies describing DT development, validation, and/or application in dental contexts were eligible. Two reviewers independently conducted screening and study selection, with a third reviewer resolving discrepancies. No automation tools were used. A total of 5989 records were retrieved, and 7 studies met the inclusion criteria. Included studies represented orthodontics, prosthodontics, endodontics, and dental education. DT applications primarily involved: patient-specific virtual modeling for diagnosis and treatment simulation, predictive or performance-monitoring frameworks using biomechanical/algorithmic analysis, and simulation-based skill training. Most were conceptual or prototype studies with small samples and limited clinical validation. DT technology has substantial potential to enhance precision, simulation, monitoring, and personalization in dentistry. However, current evidence remains constrained by fragmented research, methodological inconsistency and insufficient clinical validation. Future adoption of DT requires standardized data pipelines, robust ethical and regulatory frameworks and interdisciplinary collaboration to achieve clinically meaningful and widely adoptable DT integration in dental care.

Intrinsic capacity (IC) refers to the sum of the physical and mental capacities of an individual. Conventional IC assessment requires substantial temporal and human resources. Digital twin (DT) technology emerges as a promising solution for efficiently mapping ICs. This study aims to explore older adults' perspectives on the DT technology and their perceptions of how it could effectively represent their ICs. A qualitative study was used. Face-to-face semistructured interviews with 23 older adults were conducted. The interviews were transcribed verbatim and analyzed via content analysis approach. The analysis identified five themes and 16 subthemes: (1) "opt for or not my digital twin," revealing the older adults' decisions regarding whether to use DT technology for mapping ICs; (2) "my ideal digital avatar," describing the older adults' preferences for personalized digital avatar appearances; (3) "my digital twin maps my intrinsic capacity," highlighting how multimodal reminders and synchronized avatar changes enhanced their comprehension of ICs; (4) "the benefits my digital twin can deliver," emphasizing the potential of the DT system to provide feedback services to older adults; (5) "some expectations for my digital twin," outlining their expectations for DT technology. Based on the above insights, a conceptual model, "windmill" model, was further developed to better understand how to build DTs of older adults and map their ICs. DT technology was a promising tool for mapping ICs of older adults. Furthermore, the "windmill" model provided a framework to build tailored DTs. The findings of this study could provide references to develop DT model to support IC management.

Digital twin technology, originally developed for industrial applications, is gaining increased attention in ocean governance and multilateral negotiations. While existing research gives insights into the scope of digital ocean twin applications for environmental governance and associated technical challenges, they do not sufficiently explore how their development and use takes place across politically contentious spaces in which various public and private actors operate. To address this gap, our paper pursues two research questions: ‘Who develops and uses digital twins of the oceans and for which purposes?’ and ‘Which promises and risks are associated with digital ocean twins in the context of multilateral negotiations?’. The paper is based on empirical bibliometric research into academic literature, patents, and policy documents to identify political, scientific, and corporate actors involved in developing and applying digital ocean twins and to map their discourses. By offering a holistic view of how digital twins are, or could be, applied in ocean governance, this paper aims to contribute to the development of effective, technology-driven, but also politically sensible approaches environmental governance.

While the field of hydrography is crucial for maritime navigation and other maritime applications, oceanography is the field that provides the relevant data and knowledge for predicting climate change, monitoring marine resources, and exploring marine life. Digital ocean twins combine these two exciting fields and combine ocean observations and ocean models to establish virtual representations of a real world system, in this case the ocean or an ocean area, as well as assets in the ocean and processes within ocean industries or the natural environment. They have the potential to play a critical role in optimising and supporting sustainable ocean development. Digital Twins are synchronised with their real-world counterparts at a specific frequency and fidelity. They can provide valuable insights into the ocean's state and its evolution over time, which can be used to support decision-making in ocean governance and various ocean-related industries. Digital ocean twins can transform human ocean interactions by accelerating holistic understanding, optimal decision-making, and effective interventions. Digital twins of the ocean use ocean observations, historical and forecast data to represent the past and present and simulate possible future scenarios. They are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT systems. In this article, we explore the benefits of digital twins for the ocean, the challenges in developing them, and the current state of the art in ocean digital twin technology. One of the main benefits of digital ocean twins is their ability to provide accurate predictions of ocean conditions under expected interventions. Their information can be used to support decision- making in various applications including ocean-related industries, such as fishing, shipping, and offshore energy production. Additionally, digital twins can help to improve our understanding of the ocean's complex processes and their interactions with human activities, such as climate change, pollution, resource extraction and overfishing. Researchers and IT companies are combining various technologies and data sources, such as the Internet of Things for ocean observations, state of the art data science, artificial intelligence and machine learning, data spaces and vocabularies into digital ocean twins to contextualise data, improve the accuracy of ocean models and make ocean knowledge more accessible to a wide range of users.

Digital Twin (DT) technology has been employed as an innovator prototype in all industries; it adds to the creation of a virtual picture of physical facilities, processes and systems. This concept which evolved from the engineering areas and the manufacturing industries has extended to other fields of operation like manufacturing, health, transport, and agricultural and urban development fields. Real-time data, stream acquisition, modeling and simulation, operation optimization, decision-making improvement, analytics, and DTs allow businesses to achieve better insight. Next generation DT means next generation DT is an innovation of DT. This paper provides brief description on what DT technology is and the DT technology of the next generation, the elements of the DT at the center and how DT works. Furthermore, we consider the problems, prospects, and tendencies of the application of DTs. Hence, the paper presents a focus on the DT enabled machine learning architecture, security concerns and remedies. To justify that DT are useful for designing the future of interconnected data driven systems, examples of articles and industry are presented.

Developments in virtual technology and data acquisition technology put way to digital twin (DT) technology. Digital twin is a virtual entity that is linked to a real-world entity. Both the link and the virtual representation can be realized in several different ways. Digital Technology plays a very much key role in different areas like in production management, manufacturing, health care, smart cities and so on. Mainly Digital Twin Technology is developed to improve manufacturing processes. With the development of new-generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Digital twins facilitate to monitor, understand, and optimize the functions of all physical entities and for humans and also provide continuous feedback to improve quality of life and well-being. Digital Twin is best described as the effortless integration of data between a physical and virtual machine in either direction. This paper provides an overview of the Digital Twin technology used in different work spaces and also how it will be effective in the Internet of Things network.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

Objectives To describe the potential of digital twin (DT) technology to enhance personalised root canal treatment within endodontics, drawing upon its established applications in various healthcare sectors.Discussion DT models are gaining traction as transformative tools for enabling individualised decision-making across different medical disciplines. These models leverage multimodal patient data to simulate physiological and clinical outcomes. In endodontics, DTs could facilitate the simulation of intricate parameters, such as root canal morphology, access cavity preparation, microbial biofilm dynamics, disinfection protocols, clinical techniques for root canal preparation and filling, and the long-term behaviour of restorations. The application of DTs will empower clinicians to formulate more tailored treatment plans and improve prognostic predictions. Beyond their clinical applications, DTs can enrich research settings, linking laboratory research with tailored patient care. While deploying DTs in endodontics remains largely aspirational at this stage, it has the potential to shift the paradigm from standardised approaches to personalised treatments. Key challenges to address include data standardisation, interoperability among systems, ethical regulations, and the need for specialised clinician training. This article suggests actionable strategies for the translational development of DTs in endodontics, inspired by successful frameworks in other medical domains.Conclusion DT models can reshape the vision in endodontics, facilitating real-time, patient-specific simulation and clinical decision-making. Moreover, DT technology presents a cohesive framework that could enhance precision in endodontic practice while also expediting the translation of research findings into clinical applications. This advancement may lead to the development of personalised and predictive approaches to root canal treatment, significantly improving patient outcomes.

Construction 4.0 is witnessing exponential growth in digital twin (DT) technology developments and applications, revolutionizing the adoption of building information modelling (BIM) and other emerging technologies used throughout the built environment lifecycle. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, the DT enhances this with real-time data for integrating cyber-physical systems, enabling live asset monitoring and better decision making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study provides a holistic review of the existing research focusing on the critical components responsible for developing the applications of DT technology in the construction industry. It highlights five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, non-technical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices. It also pinpoints the latest developments in AI, namely the large language model (LLM) and retrieval-augmented generation (RAG)’s implications for DT education, policies, and the construction industry’s practices.

Following the fourth industrial revolution and recent advances in information and communication technologies, the digital twinning concept is attracting the attention of maritime academia and the maritime industry worldwide. A digital twin is a representation in digital form of a physical item, thing, or system: a vessel, a car, a wind turbine, a power grid, a pipeline, or equipment such as a thruster or an engine. One of the key initiatives at the MAAP is to apply Digital Twin technology in the development of MASS (Maritime Autonomous Surface Ship), e-navigation, ship engine room management, training, and validation of operational concepts associated with smart and autonomous ships. To this end, the progress realized in adapting and exploring digital twin (DT) technologies at MAAP will be presented. In particular, the Kognitwin technology system (a Digital Twin system) developed by Kongsberg Maritime and other systems applicable to decision-making that ensure cost-effective, safer, and sustainable operations will be described. The focus will be placed on using digital twin technology in some of the grey areas: Optimization of Fleet with Virtual Transition of Ship Control System, Enhancing the Port and Terminal Operations, Awareness Situation about Operational Parameters, End-To-End Supply Chain Optimization, Amplified Security Ensuring Safety and Better vessel design and operation.

A digital twin is a simultaneous digital reflection of object processes and states. Digital twins are usually made of objects that exist in reality or which are very near completion in a design and production process. In our research, we investigate the potential of digital twin technology for early design. Key to the early application of digital twin in design is the role of information and simulation. Since design information is valuable for predicting the future of design, we assume that design will begin to change as digital twin technologies become more and more adaptable, as designers simultaneously have digital twins of the past, present, and future. Digital twin technologies have many capabilities to support the design process at various stages from concept design to the final design. Throughout this process, architects use digital and physical models. Combined with digital twin technology, these models form what we call hybrid prototypes. Estimating that simulation has a vital impact on the design process, we raised the question of what the potential of architectural hybrid prototyping in design processes with digital twin technologies is. Similar to the development of the design through increasingly informed and detailed models, we think that the closest thing to the design process with the digital twin is the so-called foetal, child, and adult digital twin. Based on this classification, we approach the concept of hybrid prototyping and digital twin.

Surgical data science is devoted to enhancing the quality, safety, and efficacy of interventional healthcare. While the use of powerful machine learning algorithms is becoming the standard approach for surgical data science, the underlying end-to-end task models directly infer high-level concepts (e.g., surgical phase or skill) from low-level observations (e.g., endoscopic video). This end-to-end nature of contemporary approaches makes the models vulnerable to non-causal relationships in the data and requires the re-development of all components if new surgical data science tasks are to be solved. The digital twin (DT) paradigm, an approach to building and maintaining computational representations of real-world scenarios, offers a framework for separating low-level processing from high-level inference. In surgical data science, the DT paradigm would allow for the development of generalist surgical data science approaches on top of the universal DT representation, deferring DT model building to low-level computer vision algorithms. In this latter effort of DT model creation, geometric scene understanding plays a central role in building and updating the digital model. In this work, we visit existing geometric representations, geometric scene understanding tasks, and successful applications for building primitive DT frameworks. Although the development of advanced methods is still hindered in surgical data science by the lack of annotations, the complexity and limited observability of the scene, emerging works on synthetic data generation, sim-to-real generalization, and foundation models offer new directions for overcoming these challenges and advancing the DT paradigm.

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector.