Trust By Design: Enabling Responsible Precision Health Through Blockchain-Powered Digital Twins And Trusted AI

The Executive Session, Trust by Design: Enabling Responsible Precision Health through Blockchain-Powered Digital Twins and Trusted AI, explores how the convergence of blockchain, artificial intelligence, genomics, 6G wireless technology, and other advanced technologies can be leveraged to power precision health digital twins. The dialogue focused on governance, interoperability, cybersecurity, and the impact of blockchain and trusted AI-powered digital twins on advancing precision healthcare and personalized medicine. Use cases-for genomics, radiology, theranostics, and end-of-life care-illustrated both opportunities and barriers. Throughout the discussion, speakers emphasized the centrality of trust, patient sovereignty, and resilient infrastructures for the next generation of healthcare.

Precision health is about preventing, predicting, and treating diseases precisely with the principles of the right care at the right time for the right patient. Precision health is expected to help increase health equity in general. Digital Twins and Digital Triplets can significantly help in meeting the goals of precision health. In this chapter we present digital twins and digital triplets and their roles in realizing precision health. Digital twin is the digital representation of a physical object in the digital space. In the Precision Health context, digital twins are indeed enablers of machine learning and knowledge mining. It is also useful for the in-silico simulation of a person’s phenotype (health states) and genotype (molecular states) to realize evidence-based medicine. Moreover, using probabilistic graph model and neuro-symbolic AI, digital twins will be useful in mitigating physician’s knowledge gaps or decision gaps to achieve value-based care. In contrast to Digital twins, Digital triplet is the semantic intelligence about the object. Digital triplets capture the semantics by placing semantically similar objects close together in the vector embedding space. This semantic intelligence helps cognition and discover hidden and unknown knowledge and their interrelationships to make accurate clinical and medical predictions. We group digital twins in three major categories, namely, Person Phenotype Digital Twin, Person Genotype Digital Twin, and Physicians’ Brain Digital Twin. Person phenotype digital twin relates to all observable properties of a person and a population. Person genotype digital twin helps understand the molecular properties of a person and a population. Physicians brain digital twin is the doctors’ brain with actionable biomedical knowledge in the virtual space.

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.

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.

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.

Background Personalised cardiovascular medicine uses multimodal screening and diagnostic tools, spanning from organ to tissue to molecular levels, to generate a complex and detailed patient phenotype. Cardiac digital twins are powerful tools capable of merging and explore these multimodal data by developing virtual, mechanistic and predictive versions of the patient's heart. State-of-the-art pipelines are mainly based on artificial intelligence and optimization process to replicate clinical data and not used it as an input. We present a proof-of-principle pipeline for building digital heart twins based on cardiovascular magnetic resonance (CMR) imaging and integrated electrocardiographic imaging (ECGI) for diverse clinical scenarios by integrating them into the model (Fig. 1). Methods CMR at 3T was performed on participants all born in the same week in March 1946, as part of the longest-running continued surveillance birth cohort: the National Survey of Health and Development. Cine steady-state free precession end-diastolic frames provided anatomical information per participant while CMR multiparametric mapping and late gadolinium enhancement (LGE) provided tissue characteristics to the electrical myocardial model used in the finite element solver Alya. Lastly, the ECGI activation map was used as a source to define the intrinsic endocardial activation sequence by applying a case-specific correction based on wall thickness and myofibers orientation. Activation-repolarization intervals map was used to introduce the personalised repolarization heterogeneities. Results In total, 406 prospectively recruited participants (77.8 ± 0.1 years, 44% male) had multiparametric CMR with ECGI for digital twin reconstruction. The pipeline’s performance was evaluated by measuring the root mean square error (RMSE) between the ECGI activation and repolarization maps with the in-silico ones obtained without applying the proposed pipeline (Baseline) and after its application (ecgi2model). Results obtained using ecgi2model pipeline presented a significantly reduced activation and repolarization RMSE (Fig. 2b), even providing accurate results in patients with different LGE patterns (Fig. 2c). Conclusion Personalised virtual heart models can be constructed at scale using real-world myocardial structural, functional, and electrical properties, from advanced CMR and ECGI data. Proposed digital twins provide a framework for personalised arrhythmic risk assessment based on real clinical data and also its applicability for in-silico clinical trials. Fig.1 Digital twins pipeline. Fig.2 a) Flattened epicardial map. b) RMSE of activation (ACT), repolarization (RPL) and activation-repolarization intervals (ARI) for the different methods methods. c) ACT and RPL maps obtained from ECGI, baseline simulation and ecgi2model simulation in a healthy participant with normal myocardial tissue, another with subendocardial LGE, and another with subepicardial LGE.Figure 1Figure 2

Biopharma Is Going Digital … Bit by Bit

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren’t sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications.

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.

Purpose: Technological advancements are transforming the field of sports science and medicine, leading to a new era of performance improvement and injury prevention. Digital twins and artificial intelligence (AI) are at the forefront of these innovations, working together to redefine athletic training and monitoring. This editorial offers a comprehensive overview of the integration of digital twins and AI in sports science, with a focus on their potential applications, challenges, and future developments. By utilizing sensor data, AI algorithms, and biometrics, digital twins create virtual replicas of athletes, enabling precise performance monitoring and personalized training programs. Conclusions: Large datasets generated by AI can be used to predict and prevent injuries, as well as to enhance communication among stakeholders. Despite the promises, challenges such as privacy concerns and data accuracy need to be addressed. Future advancements will concentrate on sensor accuracy, AI algorithm refinement, and broader applications. The editorial highlights exciting research opportunities, including predictive injury models, real-time performance monitoring, and longitudinal health studies. Ultimately, the collaboration between digital twins and AI represents a paradigm shift in sports science, with the potential to revolutionize athlete well-being and performance optimization.

A comprehensive survey on the convergence of Blockchain, Digital Twins, and Metaverse: Shaping the future of cybersecurity frameworks

In today’s rapidly changing work environment, the introduction of digital employee twins marks an important modification regarding the way we embrace and engage fresh faces to our team. These virtual avatars, backed by artificial intelligence, are poised to change onboarding and training by providing employees with a tailored and engaging learning experience. Their applications are wide-ranging, from onboarding and training to offering assessment of achievement and mentoring. Digital twins (DTs) have emerged as an increasingly vital resource in the age of digital innovation, as the need to adjust to the shifting dynamics of building production grows. Many industries, like production, power generation, transportation, medical treatment, and many more, have shown a great deal of interest in and adoption of DT. As a result of rapid advances in communication and technology, electronic devices have woven themselves into the fabric of our daily lives. The variety and capabilities of these electronic products and services are expanding at an unprecedented pace. Today’s consumers no longer settle for “standard" solutions—they expect every good or service to be tailored to their unique needs. Continuous worker opinion collection is made simpler by DTs, which provide administrators with insightful data on satisfaction with work. Using an environmental approach like this might influence important choices that could have gone unnoticed or unnoticed. Similarly, flexibly reacting to actual-time information can be used to simulate upcoming occurrences (Piras et al., 2024).

Despite the increasing affordability of data processing and storage and the enhancement of artificial intelligence (AI) and digital technologies in recent years, scalability and adoption continue to be a challenge when it comes to digital twins (DTs). Common challenges that are often cited include the effort of designing and building DTs, high customisation, the cost to operate and maintain DTs, interoperability between DT components and DTs, and the extensive analysis and effort required to turn DT outputs into useful insights. AI has seen significant advancements and growth lately, driven by the release of popular AI products such as ChatGPT, Google Gemini and DeepSeek's R1. Many of the recent developments have the potential to address the challenges of scaling and adopting DTs. This paper examines the intersection of AI and DTs and explores how AI can be used to address some of the challenges of scaling and adopting DTs. It concludes with a set of principles that aim to apply to most DT applications, regardless of use case or industry, and proposes AI methods and techniques that can potentially be used for each principle. These principles are (1) reduce effort, cost and/or time; (2) optimise resource and system efficiency; (3) improve interaction and outcome and (4) improve interoperability, reusability and maintainability.

The aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.