数字孪生 医疗健康

Introducing the concept of digital twins in healthcare, medical education, and research is a complex multistage challenge requiring participation of multidisciplinary teams. In pursuing this goal, we have created a validated database of scans of colorectal tumor slides associated with relevant clinical and histological information. This database is also linked to the blood bank, which opens a wide range of opportunities for further research. Herein, we present our experience within the scope of the digital twins initiative.

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry.

The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties. The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials. The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

Digital twins are virtual replicas of real-world objects and processes, and they have potential applications in the field of surgical procedures, such as enhancing situational awareness. We introduce Twin-S, a digital twin framework designed specifically for skull base surgeries. Twin-S is a novel framework that combines high-precision optical tracking and real-time simulation, making it possible to integrate it into image-guided interventions. To guarantee accurate representation, Twin-S employs calibration routines to ensure that the virtual model precisely reflects all real-world processes. Twin-S models and tracks key elements of skull base surgery, including surgical tools, patient anatomy, and surgical cameras. Importantly, Twin-S mirrors real-world drilling and updates the virtual model at frame rate of 28. Our evaluation of Twin-S demonstrates its accuracy, with an average error of 1.39 mm during the drilling process. Our study also highlights the benefits of Twin-S, such as its ability to provide augmented surgical views derived from the continuously updated virtual model, thus offering additional situational awareness to the surgeon. We present Twin-S, a digital twin environment for skull base surgery. Twin-S captures the real-world surgical progresses and updates the virtual model in real time through the use of modern tracking technologies. Future research that integrates vision-based techniques could further increase the accuracy of Twin-S.

The digital twin concept is the virtual model based on entity design measures, which is used in many enterprises' virtual workshop design models for workshop production scheduling and optimization. However, in the field of medical rehabilitation, the integration of digital twin technology started late compared to traditional industrial manufacturing. Many current digital models are not well suited for information interaction between patients and devices. In order to address the lack of interaction between patients and devices in the field of medical rehabilitation, this paper proposes an automatic gait data control system (AGDCS) for fully actuated lower limb exoskeleton digital twinning. This system improves the integration of digital twinning system with the medical rehabilitation field and analyzes the patient's gait data through simulation experiments. The digital twin system was designed in several steps. Firstly, the upper computer function module was designed and developed according to the rehabilitation treatment needs. After that, the combination of exoskeleton robot and software was carried out, and finally the real rehabilitation treatment environment of patients was simulated through experiments. The proposed system was very reliable in the experimental tests of the host computer and exoskeleton robot. In the upper computer test, the patient specific gait can be generated, and the motion of the exoskeleton robot can be observed in real-time. During the walking test of the exoskeleton robot, the exoskeleton robot completed the specified gait. The result verified the superiority and effectiveness of the digital twin system AGDCS in the field of rehabilitation. The digital twin system proposed in this paper improves the interaction between self-balancing exoskeleton robot and patients, and improves the autonomy and safety of patients in rehabilitation treatment.

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans.

Digital twin represents the core technology to realize the dynamic monitoring of complex industrial systems. However, the human body, as the most complex system in the physical world, digital twin is rarely applied in it. In this study, we successfully demonstrated a digital twin in the human biomedical application by proposing a dynamic monitoring system of the upper limb force. In this system, the real upper limb drives the motion of the virtual one in real-time and dynamically updates the force. Meanwhile, the virtual upper limb feeds back the monitoring-results of the force to the controller of the real upper limb

This manuscript presents a manifesto developed by a multifaceted board of stakeholders aimed at guiding the implementation of Digital Twin (DT) technology in pathology laboratories. DTs, already transformative in other sectors, hold substantial promise for enhancing operational efficiency, diagnostic accuracy, and quality of care in pathology. We provide a comparative analysis of traditional versus DT-enhanced workflows across critical steps including accessioning, grossing, processing, embedding, cutting, staining, scanning, diagnosis, and archiving. The framework highlights measurable gains such as up to 90% reduction in labeling errors, 20-30% improvements in slide quality, and 30-50% reductions in diagnostic turnaround time. Alongside these benefits, we address key implementation challenges including upfront infrastructure costs, workforce adaptation, and data security concerns. A practical, phased deployment strategy is proposed-centered on LIS integration, IoT sensors, AI modules, and robust data governance. Estimated setup costs for a medium-sized laboratory range between USD 100,000 and USD 200,000, with a phased rollout timeline of 12-24 months. Supporting technologies like robotic process automation (RPA), collaborative robotics, and edge computing are also discussed as enablers of successful DT adoption. The manifesto closes by identifying critical research gaps, including the need for longitudinal studies evaluating DTs' clinical and economic impacts, integration within existing hospital IT systems, and ethical implications of AI-assisted diagnostics. Through this collective vision, we provide a realistic and actionable roadmap to drive the transition toward predictive, efficient, and digitally optimized pathology laboratories.

Digital twins-dynamic and real-time simulations of systems or environments-represent a paradigm shift in emergency medicine. We explore their applications across prehospital care, in-hospital management, and recovery. By integrating real-time data, wearable technology, and predictive analytics, digital twins hold the promise of optimizing resource allocation, advancing precision medicine, and tailoring rehabilitation strategies. Moreover, we discuss the challenges associated with their implementation, including data resolution, biological heterogeneity, and ethical considerations, emphasizing the need for actionable frameworks that balance innovation with data governance and public trust.

Creation of a virtual avatar of a patient with cancer has the potential to transform theranostics into a truly individualized precision treatment of specific cancers, which express targetable receptors. Each patient is unique. Their cancer molecular biology has its own inherent relationship to their genomic phenotype and the metabolomic and immunological milieu of their tumor. This singularity can be captured and simulated through generation of an avatar, incarnated by means of artificial intelligence collection, collation and analysis of personal radio-genomics, tumor pathology, and molecular biology data, in the form of a digital twin. The capacity to replicate these idiosyncratic individual interactions within a digital twin construct of such a virtual avatar allows contemplation of

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

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Aging research has primarily focused on adult aging clocks, leaving a critical gap in understanding a biological clock across the full life cycle, particularly during infancy and childhood. Here we introduce LifeClock, a biological clock model that predicts biological age across all life stages using routine electronic health records and laboratory test data. To enhance individualized predictions, we integrated virtual patient representations from 24,633,025 heterogeneous longitudinal clinical visits across 9,680,764 individuals and projected them into a latent space. Our approach leverages EHRFormer, a time-series transformer-based model, to analyze developmental and aging dynamics with high precision and develop accurate biological age clocks spanning infancy to old age. Our findings reveal distinct biological clock patterns across different life stages. The pediatric clock is strongly associated with children's development and accurately predicts current and future risks of major pediatric diseases, including malnutrition, growth and developmental abnormalities. The adult clock is strongly associated with aging and accurately predicts current and future risks of major age-related diseases, such as diabetes, renal failure, stroke and cardiovascular diseases. This work therefore distinguishes pediatric development from adult aging, establishing a novel framework to advance precision health by leveraging routine clinical data across the entire lifespan.

A digital twin is a virtual model developed to accurately reflect a physical thing or a system. In radiology, a digital twin of a radiological device enables developers to test its characteristics, make alterations to the design or materials, and test the success or failure of the modifications in a virtual environment. Innovative technologies, such as AI and -omics sciences, may build virtual models for patients that are continuously adjustable based on live-tracked health/lifestyle parameters. Accordingly, healthcare could use digital twins to improve personalized medicine. Furthermore, the accumulation of digital twin models from real-world deployments will enable large cohorts of digital patients that may be used for virtual clinical trials and population studies. Through their further refinement, development, and application into clinical practice, digital twins could be crucial in the era of personalized medicine, revolutionizing how diseases are detected and managed. Although significant challenges remain in the development of digital twins, a structural modification to the current operating models is occurring, and radiologists can guide the introduction of such technology into healthcare.

The domain of computational biomedicine is a new and burgeoning one. Its areas of concern cover all scales of human biology, physiology, and pathology, commonly referred to as medicine, from the genomic to the whole human and beyond, including epidemiology and population health. Computational biomedicine aims to provide high-fidelity descriptions and predictions of the behavior of biomedical systems of both fundamental scientific and clinical importance. Digital twins and virtual humans aim to reproduce the extremely accurate duplicate of real-world human beings in cyberspace, which can be used to make highly accurate predictions that take complicated conditions into account. When that can be done reliably enough for the predictions to be actionable, such an approach will make an impact in the pharmaceutical industry by reducing or even replacing the extremely laboratory-intensive preclinical process of making and testing compounds in laboratories, and in clinical applications by assisting clinicians to make diagnostic and treatment decisions.

Generative artificial intelligence is revolutionizing digital twin development, enabling virtual patient representations that predict health trajectories, with large language models (LLMs) showcasing untapped clinical forecasting potential. We developed the Digital Twin-Generative Pretrained Transformer (DT-GPT), extending LLM-based forecasting solutions to clinical trajectory prediction. DT-GPT leverages electronic health records without requiring data imputation or normalization and overcomes real-world data challenges such as missingness, noise, and limited sample sizes. Benchmarking on non-small cell lung cancer, intensive care unit, and Alzheimer's disease datasets, DT-GPT outperformed state-of-the-art machine learning models, reducing the scaled mean absolute error by 3.4%, 1.3% and 1.8%, respectively. It maintained distributions and cross-correlations of clinical variables, and demonstrated explainability through a human-interpretable interface. Additionally, DT-GPT's ability to perform zero-shot forecasting highlights potential advantages of LLMs as clinical forecasting platforms, proposing a path towards digital twin applications in clinical trials, treatment selection, and adverse event mitigation.

Digital twins can aid surgeons in training and in performing interventions with greater awareness and precision. The range and variety of digital twins in surgery are described, and their use across perioperative care is discussed. While largely experimental, they are beginning to show promise for the enhancement of personalized, adaptive, and data-driven surgical care. Issues relevant to the greater adoption and deployment of digital twins are all considered.

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

Type 2 Diabetes (T2D) is a complex condition marked by insulin resistance and beta-cell dysfunction. Traditional dietary interventions, such as low-calorie or low-carbohydrate diets, typically overlook individual variability in postprandial glycemic responses (PPGRs), which can lead to suboptimal management of the disease. Recent advancements suggest that personalized nutrition, tailored to individual metabolic profiles, may enhance the effectiveness of T2D management. This study aims to present the development and application of a Digital Twin (DT) technology-a machine learning (ML)-powered platform designed to predict and modulate PPGRs in T2D patients. By integrating continuous glucose monitoring (CGM), dietary data, and other physiological inputs, the DT provides individualized dietary recommendations to improve insulin sensitivity, reduce hyperinsulinemia, and support the remission of T2D. We developed a sophisticated DT platform that synthesizes real-time data from CGM, dietary logs, and other biometric inputs to create personalized metabolic models for T2D patients. The intervention is delivered via a mobile application, which dynamically adjusts dietary recommendations based on predicted PPGRs. This methodology is validated through a randomized controlled trial (RCT) assessing its impact on various metabolic markers, including HbA1c, metabolic-associated fatty liver disease (MAFLD), blood pressure, body weight, ASCVD risk, albuminuria, and diabetic retinopathy. Preliminary data from the ongoing RCT and real-world study demonstrate the DT's capacity to generate significant improvements in glycemic control and metabolic health. The DT-driven personalized nutrition plan has been associated with reductions in HbA1c, enhanced beta-cell function, and normalization of hyperinsulinemia, supporting sustained T2D remission. Additionally, the DT's predictions have contributed to improvements in MAFLD markers, blood pressure, and cardiovascular risk factors, highlighting its potential as a comprehensive management tool. The DT technology represents a novel and scalable approach to personalized nutrition in T2D management. By addressing individual variability in PPGRs, this method offers a promising alternative to conventional dietary interventions, with the potential to improve long-term outcomes and reduce the global burden of T2D.

The advent of digital twins in pharmacology presents transformative potential for precision medicine, enabling personalized treatment optimization through dynamic computational simulations of drug interactions at molecular, cellular, and patient levels. These advanced virtual replicas of a patient's biological system are designed to predict individual therapeutic responses with high fidelity, thereby moving beyond the one-size-fits-all paradigm. This paper explores the concept of digital pharmacological twins, detailing how they can integrate heterogeneous data, including multi-omic, pharmacokinetic, pharmacodynamic, clinical, and environmental information, and employing a synergy of advanced mechanistic and machine learning models. Using illustrative examples from ongoing international initiatives, this work highlights the methodological frameworks necessary for developing and validating such comprehensive predictive tools. We underscore the critical importance of model interoperability, robust data integration strategies, and rigorous validation to ensure clinical utility. Ultimately, digital pharmacological twins promise to enhance therapeutic efficacy, minimize adverse drug reactions, and accelerate the translation of pharmacological science into tangible patient benefits.

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Digital twin technology has emerged as a breakthrough development in healthcare, providing personalised transdermal drug delivery systems for chronic pain treatment. Digital twins provide accurate, customised therapy to enhance therapeutic outcomes and reduce risks by combining patient-specific computational models. This article aims to explore the applicability of digital twin technology in improving the transdermal delivery of drugs for successful chronic pain management. It is enabling personalised treatment through patient-specific simulations. By integrating physiological data with computational models, digital twins optimise drug absorption, patch application, and dosage adjustments in real-time, enhancing therapeutic outcomes while minimising side effects. Recent advancements highlight improvements in fentanyl patch optimisation, site-specific drug delivery, and thermally controlled systems. However, challenges such as ethical concerns, data security, and standardisation need to be addressed. Future research should focus on integrating AI and IoT to refine digital twin applications in precision medicine. It can be concluded from the findings of various studies that digital twin technology offers a promising future for precise and individualised transdermal drug delivery in chronic pain, paving the way for safer and more effective therapeutic interventions.

Aortic annuloplasty, involving the implantation of an external ring around the aortic root to reduce annular dimensions, is a promising treatment for aortic valve insufficiency. However, its hemodynamic effects remain underexplored due to the absence of computational models validated by experimental and clinical data. This study introduces a computational fluid-structure interaction (FSI) model of supra valvular aortic annuloplasty using 4D-flow magnetic resonance imaging (MRI). Native and post-annuloplasty conditions of idealized aortic root phantoms, including the aortic valve, were CAD-modelled and 3D-printed with elastic resin. These phantoms were tested in a mock circulatory flow-loop providing normal pulsatile physiologic conditions using a glycerol-water mixture to simulate blood viscosity. Flow and pressure data collected from sensors were used as boundary conditions for FSI simulations. Experimental velocity fields from 4D-flow MRI were compared to computational results to assess model accuracy. MRI scans of the annuloplasty model showed an increased peak systolic velocity (up to 145.4 cm/s) and localized flow alterations, corresponding to a higher pressure gradient across the valve. During regurgitation, the annuloplasty model showed broader velocity distributions compared to the native condition. The FSI simulations closely matched 4D-flow MRI data, with strong correlation coefficients (r > 0.93) and minimal Bland-Altman differences, particularly during systolic phases. This study establishes an integrative methodology combining in-vitro, in-silico, and clinical imaging techniques to evaluate aortic annuloplasty hemodynamics. The in-vitro validated digital twin framework offers a pathway for patient-specific modelling, enabling prediction of surgical outcomes and optimization of aortic valve repair strategies.

Autonomic disorders, especially those characterized by orthostatic intolerance such as Postural Tachycardia Syndrome (POTS), remain diagnostically and therapeutically challenging due to their complex pathophysiology and limited access to specialized care. This paper proposes a conceptual framework for applying digital twin technology to POTS and other autonomic disorders. A digital autonomic twin-a dynamic, virtual replica of a patient's autonomic system-offers a transformative approach to understanding, predicting, and managing these conditions. A dynamic digital twin framework integrates mechanistic and AI-based modeling utilizing continuous physiological, clinical, genetic, and patient-reported data to enhance individualized diagnosis, disease monitoring, and treatment. This system can simulate autonomic responses, predict disease trajectories, and personalize interventions. Digital twins provide real-time physiological modeling, adaptive treatment simulations, lifestyle intervention tracking, and integration of environmental and biometric data. Key components include wearable devices, electronic health records, AI-driven simulations, and clinician interfaces. However, challenges such as data volume, model transparency, and ethical considerations must be addressed. In conclusion, digital twin technology has the potential to revolutionize the management of POTS and related autonomic disorders, transitioning to personalized, predictive, adaptive medicine by providing a continuously updated and tailored approach to neurological care.

Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting. We sought to bridge this gap by linking preclinical data in a porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation. The mathematical model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time. This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings. Research to improve survival in patients with severe bleeding after major trauma presents many challenges. Here, we created a computer model to simulate the effects of severe bleeding. We refined this model using data from existing animal studies to ensure our simulations were accurate. We also used patient data to further refine the simulations to accurately predict which patients would live and which would not. We studied the effects of different treatment protocols on these simulated patients and show that treatment with plasma (the fluid portion of blood that helps form blood clots) and red blood cells jointly, gave better results than treatment with intravenous fluid or plasma alone. Early treatment with plasma reduced injury severity and increased survival time. This modelling approach may improve our ability to evaluate new treatments for trauma-associated bleeding and other acute conditions.

Accurate prenatal diagnosis of coarctation of the aorta (CoA) is challenging due to high false positive rate burden and poorly understood aetiology. Despite associations with abnormal blood flow dynamics, fetal arch anatomy changes and alterations in tissue properties, its underlying mechanisms remain a longstanding subject of debate hindering diagnosis in utero. This study leverages computational fluid dynamics (CFD) simulations and statistical shape modelling to investigate the interplay between fetal arch anatomy and blood flow alterations in CoA. Using cardiac magnetic resonance imaging data from 188 fetuses, including normal controls and suspected CoA cases, a statistical shape model of the fetal arch anatomy was built. From this analysis, digital twin models of false and true positive CoA cases were generated. These models were then used to perform CFD simulations of the three-dimensional fetal arch haemodynamics, considering physiological variations in arch shape and blood flow conditions across the disease spectrum. This analysis revealed that independent changes in the shape of. the arch and the balance of left-to-right ventricular output led to qualitatively similar haemodynamic alterations. Transitioning from a false to a true positive phenotype increased retrograde flow through the aortic isthmus. This resulted in the appearance of an area of low wall shear stress surrounded by high wall shear stress values at the flow split apex on the aortic posterior wall opposite the ductal insertion point. Our results suggest a distinctive haemodynamic signature in CoA characterised by the appearance of retrograde flow through the aortic isthmus and altered wall shear stress at its posterior side. The consistent link between alterations in shape and blood flow in CoA suggests the need for comprehensive anatomical and functional diagnostic approaches in CoA. This study presents an application of the digital twin approach to support the understanding of CoA mechanisms in utero and its potential for improved diagnosis before birth.

Performance in many racing sports depends on the ability of the athletes to produce and maintain the highest possible work i.e., the highest power for the duration of the race. To model this energy production in an individualized way, an adaptation and a reinterpretation (including a physiological meaning of parameters) of the three-component Margaria-Morton model were performed. The model is applied to the muscles involved in a given task. The introduction of physiological meanings was possible thanks to the measurement of physiological characteristics for a given athlete. A method for creating a digital twin was therefore proposed and applied for national-level cyclists. The twins thus created were validated by comparison with field performance, experimental observations, and literature data. Simulations of record times and 3-minute all-out tests were consistent with experimental data. Considering the literature, the model provided good estimates of the time course of muscle metabolite concentrations (e.g., lactate and phosphocreatine). It also simulated the behavior of oxygen kinetics at exercise onset and during recovery. This methodology has a wide range of applications, including prediction and optimization of the performance of individually modeled athletes.

Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. A short case study is presented to demonstrate the viability of the proposed simulation architecture. The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.

While current dopamine-based drugs seem to be effective for most Parkinson's disease (PD) motor dysfunctions, they produce variable responsiveness for resting tremor. This lack of consistency could be explained by considering recent evidence suggesting that PD resting tremor can be divided into different partially overlapping phenotypes based on the dopamine response. These phenotypes may be associated with different pathophysiological mechanisms produced by a cortical-subcortical network involving even non-dopaminergic areas traditionally not directly related to PD. In this study, we propose a bio-constrained computational model to study the neural mechanisms underlying a possible type of PD tremor: the one mainly involving the serotoninergic system. The simulations run with the model demonstrate that a physiological serotonin increase can partially recover dopamine levels at the early stages of the disease before the manifestation of overt tremor. This result suggests that monitoring serotonin concentration changes could be critical for early diagnosis. The simulations also show the effectiveness of a new pharmacological treatment for tremor that acts on serotonin to recover dopamine levels. This latter result has been validated by reproducing existing data collected with human patients.

The increasing availability of extensive and accurate clinical data is rapidly shaping cardiovascular care by improving the understanding of physiological and pathological mechanisms of the cardiovascular system and opening new frontiers in designing therapies and interventions. In this direction, mathematical and numerical models provide a complementary relevant tool, able not only to reproduce patient-specific clinical indicators but also to predict and explore unseen scenarios. With this goal, clinical data are processed and provided as inputs to the mathematical model, which quantitatively describes the physical processes that occur in the cardiac tissue. In this paper, the process of integration of clinical data and mathematical models is discussed. Some challenges and contributions in the field of cardiac electrophysiology are reported.

The performance of solid oral dosage forms targeting the colon is typically evaluated using standardised pharmacopeial dissolution apparatuses. However, these fail to replicate colonic hydrodynamics. This study develops a digital twin of the Dynamic Colon Model; a physiologically representative in vitro model of the human proximal colon. Magnetic resonance imaging of the Dynamic Colon Model verified that the digital twin robustly replicated flow patterns under different physiological conditions (media viscosity, volume, and peristaltic wave speed). During local contractile activity, antegrade flows of 0.06-0.78 cm s

Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a χ

Preemptive medicine represents a paradigm shift from reactive treatment to proactive disease prevention. The integration of omics technologies, the Internet of Things (IoT), and artificial intelligence (AI) has facilitated the development of personalized, predictive, and preemptive healthcare strategies. Omic technologies, such as genomics, proteomics, and metabolomics, provide comprehensive insights into molecular profile of an individual, revealing potential disease predispositions and health trajectories. IoT devices, such as wearables and smartphones, enable continuous and periodic monitoring of physiological parameters, thus providing a dynamic view of an individual's health status. AI algorithms analyze comprehensive and complex data from omics and IoT technologies to identify patterns and correlations that inform predictive models of disease risk, progression, and response to interventions. Medical digital twins, or virtual replicas of an individual's biological processes, have emerged as the cornerstone of preemptive medicine. The integration of omics, IoT, and AI enables the development of medical digital twins, which in turn allows for precise simulation of human physiological profiles, prediction of future health outcomes, and virtual individual clinical trials, facilitating personalized proactive interventions and preemptive disease control. This review demonstrates the convergence of omics, IoT, and AI in preemptive medicine, highlighting their potential to revolutionize healthcare by enabling early disease detection, personalized treatment strategies, and chronic disease prevention. We show how AI leverages omics and IoT in preemptive medicine through several case studies while also discussing the necessary data for developing medical digital twins and addressing ethical and social aspects that warrant consideration. Medical digital twins signify a fundamental transformation in health management, shifting from treating diseases after their occurrence to controlling them before their occurrence. This approach enhances the effectiveness of medical interventions and improves overall health outcomes, preparing for a healthier future.

Healthcare technologies have seen a surge in utilization during the COVID 19 pandemic. Remote patient care, virtual follow-up and other forms of futurism will likely see further adaptation both as a preparational strategy for future pandemics and due to the inevitable evolution of artificial intelligence. This manuscript theorizes the healthcare applications of digital twin technology. Digital twin is a triune concept that involves a physical model, a virtual counterpart, and the interplay between the two constructs. This interface between computer science and medicine is a new frontier with broad potential applications. We propose that digital twin technology can exhaustively and methodologically analyze the associations between a physical cancer patient and a corresponding digital counterpart with the goal of isolating predictors of neurological sequalae of disease. This proposition stems from the premise that data science can complement clinical acumen to scientifically inform the diagnostics, treatment planning and prognostication of cancer care. Specifically, digital twin could predict neurological complications through its utilization in precision medicine, modelling cancer care and treatment, predictive analytics and machine learning, and in consolidating various spectra of clinician opinions.

The development of a digital twin (DT) framework for fast online adaptive proton therapy planning in prostate stereotactic body radiation therapy (SBRT) with dominant intraprostatic lesion (DIL) boost represents a significant advancement in personalized radiotherapy. This framework integrates deep learning-based multi-atlas deformable image registration, daily patient anatomy updates via cone-beam CT (CBCT), and knowledge-based plan quality evaluation using the ProKnow scoring system to achieve clinical-equivalent plan quality with substantially reduced reoptimization times compared to traditional clinical workflows. Drawing on a database of 43 prior prostate SBRT cases, the DT framework predicts interfractional anatomical variations for new patients and pre-generates multiple probabilistic treatment plans. Upon acquiring daily CBCT, it enables rapid plan reoptimization, achieving an average reoptimization time of 5.5 [2.8, 8.2] minutes, compared to 19.8 [7.9, 31.7] minutes for clinical plans. The DT-based plans yielded a plan quality score of 157.2 [151.6, 162.8], surpassing or matching clinical plans, with superior dose coverage for the DIL (V100: 99.5%) and clinical target volume (CTV V100: 99.8%). Additionally, the framework minimized doses to organs at risk (OARs), achieving bladder V20.8Gy of 11.4 [7.2, 15.6] cc, rectum V23Gy of 0.7 [0.3, 1.1] cc, and urethra D10 of 90.9% [88.6%, 93.2%], aligning with clinical standards. By addressing interfractional variations efficiently, the DT framework enhances treatment precision, reduces OAR toxicity, and supports real-time adaptive radiotherapy. This transformative approach not only streamlines the planning process but also improves clinical outcomes, offering a scalable solution for prostate SBRT with DIL boost and paving the way for broader applications in adaptive proton therapy.

The introduction of functional in-silico models, in addition to in-vivo tumor models, opens up new and unlimited possibilities in cancer research and drug development. The world's first digital twin of the A549 cell's electrophysiology in the human lung adenocarcinoma, unveiled in 2021, enables the investigation and evaluation of new research hypotheses about modulating the function of ion channels in the cell membrane, which are important for better understanding cancer development and progression, as well as for developing new drugs and predicting treatments. The developed A549 in-silico model allows virtual simulations of the cell's rhythmic oscillation of the membrane potential, which can trigger the transition between cell cycle phases. It is able to predict the promotion or interruption of cell cycle progression provoked by targeted activation and inactivation of ion channels, resulting in abnormal hyper- or depolarization of the membrane potential, a potential key signal for the known cancer hallmarks. For example, model simulations of blockade of transient receptor potential cation channels (TRPC6), which are highly expressed during S-G2/M transition, result in a strong hyperpolarization of the cell's membrane potential that can suppress or bypass the depolarization required for the S-G2/M transition, allowing for possible cell cycle arrest and inhibition of mitosis. All simulated research hypotheses could be verified by experimental studies. Functional, non-phenomenological digital twins, ranging from single cells to cell-cell interactions to 3D tissue models, open new avenues for modern cancer research through "dry lab" approaches that optimally complement established in-vivo and in-vitro methods.

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response. Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

The development of lung digital twins (DTs) represents a significant advance in personalized medicine, providing a virtual framework that replicates the structure, function, and pathology of the respiratory system in an individualized manner. DTs integrate clinical data, high-resolution images, and mathematical models to simulate respiratory mechanics, gas diffusion, and fluid dynamics in real time. This technology improves diagnosis, treatment planning, and disease progression monitoring. One of the key applications of lung DTs is the ability to simulate patient-specific response to treatments and predict outcomes, allowing for personalized therapies. Despite advances, the implementation of DTs in clinical practice faces challenges related to data integration, computational efficiency, and ethical considerations regarding data privacy. Nevertheless, lung DTs offer clear promise for improving precision medicine, optimizing patient care, and improving clinical outcomes. El desarrollo de gemelos digitales (GD) pulmonares representa un avance significativo en la medicina personalizada, proporcionando un marco virtual que replica la estructura, la función y la patología del sistema respiratorio de manera individualizada. Los GD integran datos clínicos, imágenes de alta resolución y modelos matemáticos para simular la mecánica respiratoria, la difusión de gases y la dinámica de fluidos. Esta tecnología mejora el diagnóstico, la planificación de tratamientos y la monitorización de la progresión de enfermedades. Una de las aplicaciones clave de los GD pulmonares es la capacidad de simular la respuesta específica de cada paciente a los tratamientos y predecir los resultados, permitiendo personalizar las terapias. A pesar de los avances, la implementación de los GD en la práctica clínica enfrenta desafíos relacionados con la integración de datos, la eficiencia computacional y consideraciones éticas sobre la privacidad de la información. No obstante, los GD pulmonares ofrecen una promesa clara para mejorar la medicina de precisión, optimizar la atención al paciente y mejorar los resultados clínicos.

Digital twins (DT) are increasingly used in healthcare to model patients, processes, and physiological systems. While recent solutions leverage visualization, visual analytics, and user interaction, these systems rarely incorporate structured service design methodologies. Bridging service design with visual analytics and visualization can be valuable for the healthcare DT community. This paper aims to introduce the service design discipline to visualization researchers by framing this integration gap and suggesting research directions to enhance the real-world applicability of DT solutions.

Electrocardiogram (ECG) signals serve as a foundational data source for cardiac digital twins, yet their diagnostic utility is frequently compromised by noise and artifacts. To address this issue, we propose TF-TransUNet1D, a novel one-dimensional deep neural network that integrates a U-Net-based encoder-decoder architecture with a Transformer encoder, guided by a hybrid time-frequency domain loss. The model is designed to simultaneously capture local morphological features and long-range temporal dependencies, which are critical for preserving the diagnostic integrity of ECG signals. To enhance denoising robustness, we introduce a dual-domain loss function that jointly optimizes waveform reconstruction in the time domain and spectral fidelity in the frequency domain. In particular, the frequency-domain component effectively suppresses high-frequency noise while maintaining the spectral structure of the signal, enabling recovery of subtle but clinically significant waveform components. We evaluate TF-TransUNet1D using synthetically corrupted signals from the MIT-BIH Arrhythmia Database and the Noise Stress Test Database (NSTDB). Comparative experiments against state-of-the-art baselines demonstrate consistent superiority of our model in terms of SNR improvement and error metrics, achieving a mean absolute error of 0.1285 and Pearson correlation coefficient of 0.9540. By delivering high-precision denoising, this work bridges a critical gap in pre-processing pipelines for cardiac digital twins, enabling more reliable real-time monitoring and personalized modeling.

Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements dictated by established clinical workflows. We present a generalizable Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such Digital Twins can be predictive, modular, evolving, informed, interpretable and explainable with applications ranging from oncology to epidemiology.

The idea of a systematic digital representation of the entire known human pathophysiology, which we could call the Virtual Human Twin, has been around for decades. To date, most research groups focused instead on developing highly specialised, highly focused patient-specific models able to predict specific quantities of clinical relevance. While it has facilitated harvesting the low-hanging fruits, this narrow focus is, in the long run, leaving some significant challenges that slow the adoption of digital twins in healthcare. This position paper lays the conceptual foundations for developing the Virtual Human Twin (VHT). The VHT is intended as a distributed and collaborative infrastructure, a collection of technologies and resources (data, models) that enable it, and a collection of Standard Operating Procedures (SOP) that regulate its use. The VHT infrastructure aims to facilitate academic researchers, public organisations, and the biomedical industry in developing and validating new digital twins in healthcare solutions with the possibility of integrating multiple resources if required by the specific context of use. Healthcare professionals and patients can also use the VHT infrastructure for clinical decision support or personalised health forecasting. As the European Commission launched the EDITH coordination and support action to develop a roadmap for the development of the Virtual Human Twin, this position paper is intended as a starting point for the consensus process and a call to arms for all stakeholders.

Digital twins (DTs) are redefining healthcare by paving the way for more personalized, proactive, and intelligent medical interventions. As the shift toward personalized care intensifies, there is a growing need for an individual's virtual replica that delivers the right treatment at the optimal time and in the most effective manner. The emerging concept of a Human Digital Twin (HDT) holds the potential to revolutionize the traditional healthcare system much like digital twins have transformed manufacturing and aviation. An HDT mirrors the physical entity of a human body through a dynamic virtual model that continuously reflects changes in molecular, physiological, emotional, and lifestyle factors. This digital representation not only supports remote monitoring, diagnosis, and prescription but also facilitates surgery, rehabilitation, and overall personalized care, thereby relieving pressure on conventional healthcare frameworks. Despite its promising advantages, there are considerable research challenges to overcome as HDT technology evolves. In this study, I will initially delineate the distinctions between traditional digital twins and HDTs, followed by an exploration of the networking architecture integral to their operation--from data acquisition and communication to computation, management, and decision-making--thereby offering insights into how these innovations may reshape the modern healthcare industry.

Chronic diseases constitute the principal burden of morbidity, mortality, and healthcare costs worldwide, yet current health systems remain fragmented and predominantly reactive. Patient Medical Digital Twins (PMDTs) offer a paradigm shift: holistic, continuously updated digital counterparts of patients that integrate clinical, genomic, lifestyle, and quality-of-life data. We report early implementations of PMDTs via ontology-driven modeling and federated analytics pilots. Insights from the QUALITOP oncology study and a distributed AI platform confirm both feasibility and challenges: aligning with HL7 FHIR and OMOP standards, embedding privacy governance, scaling federated queries, and designing intuitive clinician interfaces. We also highlight technical gains, such as automated reasoning over multimodal blueprints and predictive analytics for patient outcomes. By reflecting on these experiences, we outline actionable insights for software engineers and identify opportunities, such as DSLs and model-driven engineering, to advance PMDTs toward trustworthy, adaptive chronic care ecosystems.

Digital twin technology has is anticipated to transform healthcare, enabling personalized medicines and support, earlier diagnoses, simulated treatment outcomes, and optimized surgical plans. Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. Not in patient care, however. The challenge of modeling complex diseases with multimodal patient data and the computational complexities of analyzing it have stifled digital twin adoption in the biomedical vertical. Yet, these major obstacles can potentially be handled by approaching these models in a different way. This paper proposes a novel framework for addressing the barriers to clinical twin modeling created by computational costs and modeling complexities. We propose structuring patient health data as a knowledge graph and using closed-form continuous-time liquid neural networks, for real-time analytics. By synthesizing multimodal patient data and leveraging the flexibility and efficiency of closed form continuous time networks and knowledge graph ontologies, our approach enables real time insights, personalized medicine, early diagnosis and intervention, and optimal surgical planning. This novel approach provides a comprehensive and adaptable view of patient health along with real-time analytics, paving the way for digital twin simulations and other anticipated benefits in healthcare.

This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. We use the case of public health emergency response to disease outbreaks and epidemic control. There is potential to take advantage of the increasing availability of data and digitization to develop advanced AI methods for analysis and prediction. Using an AI systems perspective, we review emerging trends in AI systems and digital twins and propose an initial augmented AI system architecture to illustrate how an AI system can work with a 3D digital twin to address public health goals. We highlight scientific knowledge discovery, continual learning, pragmatic interoperability, and interactive explanation and decision-making as essential research challenges for AI systems and digital twins.

Digital Twin is a virtual replica of a physical system that uses real-time sensor data along with historical data to predict and simulate behaviors. In healthcare, the Digital Patient Twin represents a complete virtual copy of an individual’s medical records updated in real-time with data from tests and sensors embedded in wearable devices. Integrated care offered by traditional healthcare systems encounters a challenge in providing real-time monitoring due to the lack of interoperability between IoT devices and electronic health record (EHR) systems. Machine learning (ML)-based predictive modeling facilitates timely interventions and personalized treatment strategies, which have been shown to improve patient outcomes. Fog computing enhances the speed of data processing, which is based on IoT, but also poses many security issues in the following ways: This project investigates how Digital Twin technology can solve these challenges through the power of real-time monitoring, providing personalized care while enhancing IoT-connected healthcare security. Along with this, it also works on advanced predictive time series analytics, virtual scenarios for the optimization of IoT sensor networks, securing patient data against threats, etc. The project also integrates the use of cloud and fog infrastructures to facilitate scalable data storage, high availability, and rapid processing of data for real-time insights and decision-making.

Digital twin (DT) technology is revolutionizing healthcare systems by leveraging real-time data integration and advanced analytics to enhance patient care, optimize clinical operations, and facilitate simulation. This study aimed to identify key research trends related to the application of DTs to healthcare using structural topic modeling (STM). Five electronic databases were searched for articles related to healthcare and DT. Using the held-out likelihood, residual, semantic coherence, and lower bound as metrics revealed that the optimal number of topics was eight. The “security solutions to improve data processes and communication in healthcare” topic was positioned at the center of the network and connected to multiple nodes. The “cloud computing and data network architecture” and “machine-learning algorithms for accurate detection and prediction” topics served as a bridge between technical and healthcare topics, suggesting their high potential for use in various fields. The widespread adoption of DTs in healthcare requires robust governance structures to protect individual rights, ensure data security and privacy, and promote transparency and fairness. Compliance with regulatory frameworks, ethical guidelines, and a commitment to accountability are also crucial.

No abstract available

The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based Systems Engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.

The integration of consumer-centric digital twin (CCDT) technology is set to transform healthcare systems, enabling blockchain-based solutions for enhanced medical services. By precisely simulating patients and medical processes, CCDT establish a link to the virtual healthcare environment, supporting improved diagnosis, real-time monitoring, and predictive analytics. Blockchain technology is critical in connecting the physical and virtual health care worlds, providing secure storage, efficient communication, reduced computational costs, and reliable hosting services. However, the security of patient data and its digital twin counterpart stored on the blockchain remains a potential vulnerability, as any alteration or unauthorized access to this information could pose significant risks. To address this challenge, we propose a blockchain-enabled quantum encryption protocol for CCDT healthcare networks to ensure secure communication. The proposed protocol leverages blockchain to authenticate patients without relying on third-party entities. At the same time, a secure quantum encryption mechanism ensures that CCDT do not need to authenticate repeatedly when interacting with multiple healthcare providers. A performance evaluation demonstrates the effectiveness of the proposed scheme compared to existing encryption methods. The results indicate that the suggested protocol is resilient against various security threats, showcasing its significant potential for enhancing communication security within CCDT healthcare networks.

Rapid progress in healthcare technology has enabled the adoption of digital twin (DT) systems, allowing accurate simulations and real-time monitoring of patients through their virtual counterparts. Nonetheless, the vast amount of data generated in DT-based healthcare, coupled with privacy concerns and potential threats from quantum computing, poses significant challenges for secure data management. To address these issues, this article introduces a novel framework, lightweight authentication for postquantum secure blockchain in DT healthcare (LAPQ-BDTH). The proposed scheme utilizes lattice-based cryptography to provide quantum-resistant security and leverages blockchain with smart contracts to achieve immutable, decentralized storage of DT data, thereby improving integrity and transparency. The performance of LAPQ-BDTH is evaluated against several existing authentication and postquantum schemes, demonstrating its advantages in computational efficiency and communication overhead. Extensive security analysis and performance evaluation confirm that LAPQ-BDTH effectively preserves data confidentiality and integrity. This research lays the foundation for developing secure, resilient, and future-ready healthcare systems.

The development of digital twin (DT) systems for healthcare presents several challenges, particularly in ensuring data protection and communication security in real‐time environments. The protection of patient information in the case of future quantum‐powered attacks is one of the key issues, with traditional public‐key cryptography tools being likely to be weakened in the context of the huge quantum computers. The objective of this research was to come up with a quantum‐resistant security system to DT‐based remote healthcare monitoring. With quantum‐safe session key establishment with QKD or lattice‐based postquantum interactions, along with symmetric authenticated encryption, secure far‐edge, near‐edge and cloud data transfer and processing was guaranteed. The results revealed a 40% reduction in latency, a 30% improvement in throughput and a 15% increase in system efficiency, demonstrating substantial enhancements in performance. The integration of quantum‐resistant protocols provided robust protection without compromising system operation, achieving a 25.77% improvement in computational efficiency. The proposed framework significantly enhances the security, scalability and performance of remote healthcare systems, offering a future‐proof solution against quantum computing threats.

No abstract available

Digital Twins (DTs) play a crucial role in context-aware Internet of Things (IoT) applications within the healthcare sector, including the industrial healthcare domain, by facilitating the continuous sharing of sensitive and confidential patient data from physical objects in real time. This shared data is essential for treatment planning and decision-making processes, often being accessed remotely by authorized users. However, traditional security mechanisms, which rely on the integer factorization problem (IFP) and the elliptic curve discrete logarithm problem (ECDLP), are vulnerable to quantum attacks using algorithms like Shor's, posing significant risks to data protection. As a result, the healthcare sector faces several security challenges, including the vulnerability of sensitive patient data to cyberattacks, quantum threats, the risk of unauthorized access to medical devices and IoT systems, and the increasing sophistication of cybercriminals exploiting weak authentication methods. To address these issues, we propose a quantum-resistant protocol that safeguards data privacy in DT-enabled IoT healthcare applications, ensures secure transmission of information, maintains patient trust, supports long-term data confidentiality, and protects medical devices and IoT systems from potential breaches. By employing lattice-based cryptographic techniques, particularly the ring learning with errors (RLWE) problem, the proposed scheme effectively addresses contemporary security challenges, including those posed by quantum computing. Real-time experiments conducted on Raspberry Pi 4 devices, along with computational overhead analysis, demonstrate the protocol's efficiency. Additionally, formal security validation using the Scyther tool and security analysis with the RoR model reinforce the robustness of the proposed protocol. A comprehensive comparative evaluation against existing schemes highlights its lightweight, scalable, and efficient nature. Furthermore, performance evaluations in the context of unknown attacks show that the proposed scheme significantly outperforms current alternatives in terms of effectiveness.

No abstract available

The rapid implementation of the fifth-generation wireless networks has driven advances in digital twin (DT) technique, which has been widely used, especially in healthcare. However, the accessibility of data raises concerns about privacy, security, and accountability among participants, affecting overall security and performance of the healthcare DT system. In this article, we investigate a blockchain-based secure healthcare digital twin data (HDTD) sharing framework to address data privacy concerns. In the blockchain-based secure HDTD sharing model, we propose the access control scheme through cloud storage and attribute encryption to realize the secure data interaction between different users. Based on this, we design an HDTD missing value prediction algorithm in order to solve the problem of missing valid data due to data tampering or loss with limited resources and to meet the real-time requirements of data interaction in DT. The experimental results show that compared with the existing schemes, the proposed blockchain-based secure HDTD sharing scheme has superior performance in improving data security and reducing data interaction delay. The article outlines key technical challenges and future directions for blockchain-based HDTD research.

This article explores the user-centered theory in computational smart healthcare service systems (SHSS) and proposes a comprehensive SHSS solution based on digital twin (DT) and decentralized federated learning (DFL). To clarify the interaction between users, products, and services in SHSS, a hybrid framework called user-centered data-driven product and smart healthcare service system (UDP-SHSS) is proposed. The key challenges related to medical data privacy protection, real-time user state monitoring and data updates, and the integration of resources across decentralized medical institutions are identified through the analysis of this framework. To address these challenges, the DT-empowered DFL Framework is designed, where DT maps physical hospital entities to the virtual decentralized network and DFL offers a decentralized model interaction scheme to the decentralized network. To construct the dynamic real-time and privacy-preserving objectives of UDP-SHSS, the DT-based decentralized online federated learning (DT-DOFL) algorithm is developed, which models the framework with graph theory to account for the real-world communication scenarios between clients and incorporates differential privacy (DP) mechanisms to ensure user privacy. Given the black-box nature of DFL models, the one-point bandit feedback (OPBF) mechanism is employed to address the online convex optimization problem in UDP-SHSS. Rigorous proofs demonstrating that the algorithm achieves $\epsilon$-DP and sublinear convergence are given. Finally, several experiments on various diseases in SHSS are conducted to verify the effectiveness of our framework and algorithm.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient’s Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient’s electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

Human digital twin bridges humans with digital avatars in the fabric metaverse, assisting users and healthcare professionals with real-time visualization, analysis, and prediction of personal data sensed by fabric sensors. The human digital twin-assisted healthcare monitoring (HHM) service refreshment refers to sending personal health data to corresponding services hosted on nearby edge servers and receiving the results to update local digital avatars continuously. However, the malicious nature and resource limitations of edge servers may lead to user privacy leaks and refreshment timeout, thereby impacting diagnostics. In this paper, we investigate a novel privacy-enhanced HHM service refreshment maximization problem in the fabric metaverse by considering privacy data encryption, model compression, and personalized user requirements. To this end, we first formulate the above issue as an Integer Linear Programming (ILP) problem, and prove its NP-hardness. Then, a resource scheduler named Wiper is designed, consisting of a shallow-deep distiller and an agile refresher library. To enable efficient inference while preserving user privacy, the former replaces violation modules in existing models with approximations and conducts shallow distillation on model layers to meet operation type and depth limits of homomorphic encryption, and then deep distillation on model parameters to decrease end-to-end refreshment delay. Finally, to satisfy user requirements on accuracy and delay during encrypted refreshments while maximizing the throughput of HHM services in offline and online situations with different problem scales, a series of HHM service refreshment algorithms are merged into the latter, including exact, performance-guaranteed approximation, and residual diffusion reinforcement learning algorithms. Theoretical analyses and experiments demonstrate that our algorithms are promising compared with baseline algorithms.

Abstract The research problem of this paper was whether medical image, behavioral pattern, and physiological data analysis further artificial intelligence-based disease progression prediction, big medical data analysis and processing, and treatment planning optimization, digital twin- and generative artificial intelligence-based disease progression prediction and medical process simulation, patient outcome and pathological condition improvement, and medical service efficiency and resource allocation. We show that physiological measurement indicator modeling and simulation and patient diagnosis and clinical workflow optimization necessitate generative artificial intelligence- and machine learning-based metaverse wearable and implantable medical devices. Our analyses debate on medical metaverse digital twin generative artificial intelligence and machine learning-based big clinical and medical imaging data interoperability and analysis harnessed in remote medical treatment and healthcare practices, healthcare delivery and patient outcome enhancement, real-time medical anomaly detection, timely medical treatment and response prediction, and immersive medical procedure and healthcare delivery simulation in blockchain Internet of Things wearable sensor and computer vision-based extended reality healthcare metaverse. Our results and contributions clarify that clinical decision support systems and generative artificial intelligence-based patient medical disease and health data processing and analysis configure clinical patient care and outcome prediction, health risk forecasting, medical abnormality detection, and remote patient vital sign and health issue monitoring.

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time, and tracking their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance’s location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle’s next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN’s key role in significantly reducing the witnessed gap within the HITS’s DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%.

The healthcare industry is increasingly adopting digital twin (DT) technology, a specialized form of simulation modeling that has gained traction in industrial applications. An intelligent architecture inspired by DTs has been proposed to analyze unusual visual, behavioral, and physiological phenomena in individuals with anxiety disorders within smart office environments. This framework utilizes quantum probability methods for anomaly detection and temporal data mining for data granule framing. Additionally, a novel multilevel convolutional neural network (CNN) is introduced to predict the Health Vulnerability Index, along with a smart alert system designed to notify caregivers of detected health irregularities and provide supportive care. The method was evaluated on a challenging dataset of 59867 instances, demonstrating superior performance compared to state-of-the-art techniques in several metrics, including Temporal Efficacy (9.2 ms), Classification Efficacy (Precision: 96.29%, Sensitivity: 93.62%, Specificity: 96.73%), Decision-making Efficiency (r2: 78%, Error: 0.30%), and Stability (72%).

Digital Twin (DT) represents a synchronized digital replica of a real system, object, or process in a virtual environment, thus reflecting the physical characteristics, dynamics, and behaviors of the real world. It allows for analysis of the system's current state, predicts future behaviors using AI/ML models, and optimizes control through bidirectional communication with the actual system. Utilizing DTs in healthcare enables quantitative analysis of essential life processes, offers personalized care and predictive health analytics, and enhances strategies for disease treatment. However, the application of DTs in healthcare is still in its early stages and faces significant research challenges across various aspects. This paper proposes the architectural framework for implementing DT in IoT-based healthcare networks, as well as model for sensing/actuating and network operation DTs. We have deployed an open-source DT platform to design and test this proposed framework.

Healthcare digital twin (HDT) is an emerging technology that can improve human life by enabling disease prediction, real-time diagnosis, and personalized prescription from healthcare data of consumer electronics, such as wearable sensors and medical devices. However, the continuous exchange of sensitive data from consumer devices to centralized hospital servers can raise significant security challenges and erodes user data sovereignty, which cannot be fully addressed by existing authentication schemes. Thus, we propose a novel blockchain-based mutual authentication scheme that provides secure communication and data sovereignty for healthcare users. The proposed scheme integrates decentralized identifiers, verifiable credentials, and elliptic curve cryptography to establish secure and user-centric access control between consumer healthcare devices and medical services. To prove the security and robustness against various attacks of the proposed scheme, we perform various formal and informal analysis. Furthermore, performance evaluations show that the proposed scheme achieves lower computational overhead compared to related schemes, confirming the suitability for resource-constrained consumer devices. We also conduct simulation and implementation studies to prove the practical deployment of the proposed scheme. Thus, the proposed scheme offers practical and secure communications for building trustworthy, user-centric healthcare services, paving the way for next-generation personalized medicine by consumer electronics technology.

In a time of heightened healthcare digitization, the necessity for resilient and scalable security solutions is critical. This study presents an innovative methodology for healthcare security through the combination of Quantum-Inspired Blockchain with Digital Twin models. Quantum-inspired algorithms augment conventional blockchain by enhancing data encryption, accelerating validation times, and bolstering resistance to quantum attacks, thereby enabling secure real-time transmission of patient data. Simultaneously, the Digital Twin idea is utilized to generate accurate virtual replicas of patients and healthcare environment, facilitating proactive monitoring, simulation, and diagnosis. The integration of these technologies provides a safe, transparent, and efficient framework that tackles critical issues such data integrity, privacy, and real-time healthcare monitoring. The experimental results indicate an 50% improvement in data encryption time and a 40% decrease in validation time, illustrating the system's exceptional performance in securing and streamlining healthcare operations, representing a substantial advancement in future-proofing healthcare infrastructures.

No abstract available

Background Digital twin (DT) technology holds significant promise for healthcare systems (HSs) due to real-time monitoring based on streaming operational data and a priori analysis capabilities without interrupting clinical workflows. However, the sociotechnical complexity of HSs presents challenges for effective DT implementation. A dichotomy also exists between the engineering and implementation science (IS) communities regarding DT implementation challenges. This study assesses the efficacy of the updated Consolidated Framework for Implementation Research (CFIR 2.0) in identifying DT implementation challenges, aiming to bridge the knowledge gap between IS and DT communities. Methods This study presents findings from a DT implementation case study in a family medicine clinic, an operational healthcare microsystem. It adopts CFIR 2.0 to guide semi-structured interviews with four key stakeholder groups (e.g., family medicine specialists, engineers, organizational psychologists, and implementation scientists). Participants (N = 8) were purposively sampled based on their roles in DT implementation. Thematic coding categorized interview data into seven themes: technological, data-related, financial and economic, regulatory and ethical, organizational, operational, and personnel. Thematic data were then cross-analyzed with challenges documented in DT literature to assess how effectively CFIR 2.0 identifies DT implementation challenges. Results Challenges were grouped into three categories: (i) shared challenges captured by both IS and DT communities, (ii) CFIR 2.0-identified challenges overlooked in DT literature, and (iii) challenges documented in DT research but not captured through CFIR 2.0-guided interviews. While there was strong overlap between the communities, a formidable gap also remains. CFIR 2.0 effectively identified a diverse set of issues—predominantly in organizational, financial, and operational themes—including many overlooked by the DT community. However, it was less effective in capturing technological and data-related barriers critical to DT performance, such as modeling, real-time synchronization, and sensor reliability. Conclusions CFIR 2.0 effectively identifies organizational and operational barriers to DT implementation in healthcare but falls short in addressing technological and data-related complexities. This study highlights the need for interdisciplinary collaboration for the successful transition of emerging DT technologies into practice to maximize their impact on HS efficiency and patient outcomes.

The increasing adoption of Internet of Things (IoT) devices in healthcare has led to the emergence of Digital Twin technologies as a revolutionary approach to enhance healthcare monitoring and decision-making. In this paper, we present a hybrid ML model combining CNNs and LSTM networks with an attention mechanism to process IoT healthcare data. Using a real-world IoT dataset, we developed a framework capable of predicting device capacity and identifying anomalies. The proposed model achieved a MAE of 0.0896 and an R2 score of 0.6261, outperforming existing models. This study demonstrates the potential of digital twins in creating context-aware intelligent systems for personalized healthcare monitoring and diagnostics.

No abstract available

Digital twins (DT) in manufacturing, healthcare, and across different industrial domains are often over-simplified as solely a virtual representation of a physical object or service. Such a definition constitutes a dilemma in distinguishing DTs from digital models, digital shadows, digital threads, and cyber-physical systems. In this article, we aim to elucidate the concept of digital twins and its definition. Therefore, we go through the connotation of digital twins, which has its roots in space exploration and product-life cycle management, and describe the four evolution stages of DT developments. This article employs an ontological approach to clearly and comprehensively define digital twins and related key concepts, including digital models, assets, prototypes, shadows, and threads. Additionally, it presents a structured framework detailing the meta-model and the reference-level ontology of digital twins. To evaluate the proposed structure, definitions, and its important entities, we have examined our framework against 73 peer-reviewed papers in the healthcare sector from 2018 until July 2024. The evaluation and classification criteria of the selected works were based on four research questions. These criteria are driven by the core definitions provided by the main cross-domain digital twin researchers and this article’s proposed anatomy of digital twins.

The aim of this research is to enhance digital twin research, specifically in healthcare and smart health sectors. The main objective is to bridge the gaps in digital twin research in real-world healthcare units by proposing a new data model for a healthcare service digital twin. The research highlights the importance of secure and efficient database design through a case study at Hat Yai-Na Mom Hospital's chemotherapy centre. This innovation integrates a digital twin into healthcare and fills the gaps in literature on data systems for real healthcare units. The use of SQLite for database development adds uniqueness, making this research a pioneering effort in implementing digital twins in a healthcare context. Overall, the research provides valuable insights into database design considerations and possible applications in service system monitoring.

The concept of digital twin has become a pillar of healthcare research over the last decade. By mirroring real systems through a range of functionalities–from tracking the state of physical assets to providing cognitive services–it enables real-time analyses, thereby supporting informed decision-making. In this paper we are specifically interested in the human digital twin as one of the main component into the personalised healthcare framework. Even though literature is full of examples discussing this issue, a unified reference model and supporting technology are still lacking. As a result, a substantial gap remains between theoretical developments and practical implementation, with real-world applications still limited. Accordingly, this paper presents a human digital twin model, along with a practical implementation on the White Label Digital Twin platform, designed to be compliant to the reference standards and general enough to be specified and instantiated for different healthcare domains. As a proof of concept, we present an application in the context of hypertensive patient care where data are acquired from two different data sources and integrated into a unique standardised model.