Ethics: essential infrastructure for governance of Web3 and the metaverse in the age of AI

Biopharma Is Going Digital … Bit by Bit

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.

Background: The growing availability of high-resolution imaging, biosensors, molecular profiling, and artificial intelligence has enabled the development of digital patient twins—computational models that reproduce individual physiological and pathological processes in silico. While digital twins have been widely proposed as tools for personalised medicine, their clinical and translational value across major disease domains has not yet been systematically synthesised. Methods: A narrative review was conducted of full-text publications from 2020–2025 addressing digital patient twins in cardiology, oncology, chronic disease management, and rehabilitation. The analysed literature included translational and clinical studies, mechanistic modelling papers, and healthcare system implementations. Evidence was prioritised from studies reporting patient-specific simulations, comparisons with real clinical or imaging data, and therapy-support scenarios. Results: In cardiology, electrophysiological and haemodynamic digital twins demonstrated high concordance with invasive mapping and imaging data and were associated with improved ablation planning, device optimisation, and reduced arrhythmia recurrence. In oncology, tumour digital twins integrating imaging and molecular data predicted tumour growth and treatment response with clinically meaningful accuracy, supporting personalised and adaptive cancer therapy. In chronic diseases, sensor-driven digital twins enabled early detection of physiological deterioration and supported proactive intervention, reducing exacerbations and hospitalisations. In rehabilitation, biomechanical and neurophysiological digital twins improved functional recovery by guiding personalised and robot-assisted therapy. Conclusions: Digital patient twins are transitioning from experimental computational tools to clinically relevant systems capable of influencing diagnosis, therapy selection, monitoring, and patient outcomes. By enabling in silico testing of therapeutic strategies on a virtual representation of the patient, digital twins reduce uncertainty in clinical decision-making and support truly personalised care. Continued progress in data integration, model validation, and regulatory governance will be essential for their safe and widespread adoption in clinical practice.

The convergence of Virtual Reality (VR), Artificial Intelligence (AI), and the Internet of Things (IoT) offers transformative potential across numerous sectors. However, existing studies often examine these technologies independently or in limited pairings, which overlooks the synergistic possibilities of their combined usage. This systematic review adheres to the PRISMA guidelines in order to critically analyze peer-reviewed literature from highly recognized academic databases related to the intersection of VR, AI, and IoT, and identify application domains, methodologies, tools, and key challenges. By focusing on real-life implementations and working prototypes, this review highlights state-of-the-art advancements and uncovers gaps that hinder practical adoption, such as data collection issues, interoperability barriers, and user experience challenges. The findings reveal that digital twins (DTs), AIoT systems, and immersive XR environments are promising as emerging technologies (ET), but require further development to achieve scalability and real-world impact, while in certain fields a limited amount of research is conducted until now. This review bridges theory and practice, providing a targeted foundation for future interdisciplinary research aimed at advancing practical, scalable solutions across domains such as healthcare, smart cities, industry, education, cultural heritage, and beyond. The study found that the integration of VR, AI, and IoT holds significant potential across various domains, with DTs, IoT systems, and immersive XR environments showing promising applications, but challenges such as data interoperability, user experience limitations, and scalability barriers hinder widespread adoption.

Innovation, effective management of change, and integrating human factor elements into flight operations control distinguishing features of the aviation sector. Immersive technologies (Augmented, Virtual, and Mixed Reality – Digital Twins technology) can be used in aviation training programs to provide an immersive and interactive learning experience for all aviation professionals. Adapting an aviation immersive technology environment in transportation simulation can allow the implementation of new training approaches in a safe and controlled environment without the risk of actual flight or equipment damage. Digital twins are used to create realistic flight simulations, allowing aviation ecosystem actors to practice their skills in various scenarios and conditions. This helps to improve safety and prepare aviation experts for unexpected events during actual flight. Another use for Augmented, Virtual, and Mixed Reality Simulation in aviation training programs is maintenance training. Moreover, Digital Twins can simulate maintenance procedures on aircraft and aviation systems, allowing SMEs to enhance their knowledge and practice their skills in a safe, cost-effective, and controlled environment. Purdue University School of Aviation and Transportation Technology (SATT) Ecosystems' Artificial Intelligence (AI) research roadmap aims to introduce digital twins in aviation training programs to simulate flight-airport operations and air traffic scenarios. Moreover, Purdue's Artificial Intelligence approach for Augmented, Virtual, and Mixed Reality Simulation / Digital Twins focuses on the potential to improve the effectiveness and efficiency of aviation training programs (CBTA globally) by providing a more realistic and immersive learning experience {lean process for training/certification, transition to AI – Advanced Air Mobility (AAM) environment}. Furthermore, this research focuses on implementing challenges and mitigating residual risk in the 'AI black box.' Results were analyzed and evaluated the Artificial Intelligence certification and learning assurance challenges under the Augmented, Virtual, and Mixed Reality Simulation – Digital Twins aspects.

This chapter presents a Digital Twin Pipeline Framework of the COGNITWIN project that supports Hybrid and Cognitive Digital Twins, through four Big Data and AI pipeline steps adapted for Digital Twins. The pipeline steps are Data Acquisition, Data Representation, AI/Machine learning, and Visualisation and Control. Big Data and AI Technology selections of the Digital Twin system are related to the different technology areas in the BDV Reference Model. A Hybrid Digital Twin is defined as a combination of a data-driven Digital Twin with First-order Physical models. The chapter illustrates the use of a Hybrid Digital Twin approach by describing an application example of Spiral Welded Steel Industrial Machinery maintenance, with a focus on the Digital Twin support for Predictive Maintenance. A further extension is in progress to support Cognitive Digital Twins includes support for learning, understanding, and planning, including the use of domain and human knowledge. By using digital, hybrid, and cognitive twins, the project’s presented pilot aims to reduce energy consumption and average duration of machine downtimes. Data-driven artificial intelligence methods and predictive analytics models that are deployed in the Digital Twin pipeline have been detailed with a focus on decreasing the machinery’s unplanned downtime. We conclude that the presented pipeline can be used for similar cases in the process industry.

The integration of Artificial Intelligence (AI) with Digital Twin (DT) technology has redefined the engineering landscape by enabling real-time, data-driven decision-making across the entire lifecycle of assets. This book chapter presents an in-depth exploration of AI-driven Digital Twin systems as transformative enablers of predictive modeling, intelligent control, and sustainable asset management. It addresses the evolution, architecture, and functional frameworks of Digital Twins while emphasizing the convergence of AI methodologies, including machine learning, deep learning, and reinforcement learning, in achieving adaptive and autonomous operations. The discussion extends to predictive maintenance, fault diagnosis, and Remaining Useful Life (RUL) estimation models that enhance operational reliability and performance resilience. Security, privacy, and trust mechanisms within interconnected Digital Twin ecosystems are analyzed to ensure data integrity and safe information exchange. The chapter also highlights standardization and interoperability protocols that support seamless integration across heterogeneous industrial platforms. A comprehensive focus is placed on the application of Digital Twins throughout the engineering asset lifecycle—from conceptual design and development to operational optimization and end-of-life management. AI-enhanced simulations enable real-time optimization, while data-driven prescriptive analytics facilitate intelligent decision-making for resource utilization and performance enhancement. The fusion of Digital Twin systems with circular economy principles advances environmental sustainability by promoting closed-loop material flows, energy efficiency, and reduced carbon footprints. Emerging challenges in large-scale implementation, such as computational scalability, data governance, and ethical AI adoption, are also discussed to provide a holistic understanding of this evolving domain. The chapter concludes that AI-enabled Digital Twins represent a paradigm shift toward intelligent, self-adaptive, and sustainable engineering systems. By bridging the gap between physical and digital domains, these systems are establishing the foundation for next-generation industrial transformation that aligns innovation with sustainability, resilience, and operational excellence.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

G protein-coupled receptor digital twins for precision and personalized medicine

Artificial intelligence has been widely used to enable predictive maintenance. However, AI systems need a large amount of data to generate accurate results that can be used reliably in terms of data quality. One of the ways to obtain data from the system is through the development of a digital twin. Therefore, a digital twin design might be of key value for the predictive maintenance of systems enabling the simulation of the system’s performance, anticipating potential malfunctions, and consequently reducing the cost of unforeseen failures of the physical system. In this paper, we present a framework of a digital twin system for a conveyor belt along with different sensors that collect various types of data to be analyzed by a digital system. This way, the digital twin can generate more data focusing on reducing the time to obtain enough data to train the AI algorithm properly. Furthermore, the digital twin model is designed to develop the simulation environment and integrate it with the physical system.

A pipeline for developing digital cardiac twins integrating cardiovascular magnetic resonance and electrocardiographic imaging: results from the MyoFit46 study

A digital twin is an artificial intelligence (AI)- or human-controlled avatar that replicates a specific person’s identity. While organizations have used digital twins of employees (e.g. digital doctors answering patients’ questions), a more common use is celebrity-as-a-service, where digital twins of celebrities interact with customers. Unlike traditional celebrity endorsements in marketing videos, these digital-twin agents interact directly with consumers. Although digital twins do not enhance the underlying AI or human agents’ capabilities, their appearance as a specific person can influence users’ perception. Across two experiments, we found that when a digital twin resembled a celebrity, regardless of whether it was AI- or human-controlled, it was perceived to be more capable, benevolent, and trustworthy, leading to greater user engagement. Even when errors occurred, the celebrity’s likeness reduced negative reactions, suggesting that celebrity effects can buffer against algorithm aversion. Practically, digital celebrity twins offer business value comparable to traditional celebrity endorsements, influencing user trust and service adoption despite identical underlying performance.

The digital twin (DT) connects the physical world with the virtual world and is an integral part of the smart workshop. Existing digital twin modeling methods focus on the detailed description of modules and the interaction between modules, without considering the subject status of data, which limits the practical application of digital twins. Data-centric digital twin modeling methods can be well combined with computer programming languages to guide the implementation of digital twin applications. With the continuous advancement of big data and artificial intelligence, there is a need for an approach to modeling multi-dimensional digital twins at the conceptual level. To this end, this paper proposes a digital twin concept modeling method based on Artifact, which connects the five-dimensional digital twin framework and Web service semantics, which not only describes complex digital twin components, but also discovers Web service semantics to guide the implementation of programming languages. Based on this model, this research designs a digital twin concept modeling prototype system and models the digital twin of the warehouse workshop.

Background Digital twins are gaining ever-increasing attention from academies and industries to standardization bodies worldwide owing to their great capabilities and fundamental values in the coming fourth industrial revolution. However, there is no consistent set of definitions or concept system of the digital twin domain yet. Especially, the polysemy problem of the term “digital twin” is leading to ambiguities and an obstacle to standardization. Methods This paper summarizes the principles and guidelines of developing a concept system mentioned in two international standards, enriches them to a methodology of developing a concept system with (1) system thinking viewpoints and systems engineering methods, (2) procedures for analyzing the semantic relationships among candidate superordinate concepts, and (3) a three-dimensional taxonomy framework with procedures of developing a taxonomy, and proposed a maturity level model for terminology work. Results This paper analyzes the polysemy phenomenon of the term "digital twin”, identifies the necessity to differentiate digital twin entity and digital twin system from the general term “digital twin”, and proposes a disciplinary definition of "digital twin". After analyzing twenty-one definitions of digital twin and summarizing ten superordinate concepts from them, this paper proposes that a digital twin entity is a kind of digital asset not just a digital representation. Based on the new superordinate concept and definition of digital twin entity, a systematic digital twin concept system is developed with 210 concepts and fifty definitions. Conclusions This work resolves the polysemy problem of the term “digital twin” and demonstrates the effectiveness of the enhanced methodology of developing a concept system. The proposed digital twin concept system could be a benchmark for future digital twin terminology work and useful input to the development of digital twin system reference architecture standard and lays a solid foundation for future concept systems development of other domains.

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.