Redefining the Patient in the Digital Twin Age: A Scoping and Conceptual Model for Flexible, Ethical, and Inclusive Healthcare Systems  

Redefining the Patient in the Digital Twin Age: A Scoping and Conceptual Model for Flexible, Ethical, and Inclusive Healthcare Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Redefining the Patient in the Digital Twin Age: A Scoping and Conceptual Model for Flexible, Ethical, and Inclusive Healthcare Systems Atantra Das Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8511797/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The emergence of the Digital Twin (DT) in healthcare signifies a revolutionary change in how patients are represented, managed, and governed within AI-driven systems. Traditionally, healthcare relied on episodic data and static diagnoses; now, the Digital Twin develops a dynamic, continuously learning digital replica of the patient—merging biological identity, data infrastructure, and algorithmic analysis. Although increasingly important in precision medicine and clinical simulations, the theoretical foundations and ethical oversight of Digital Twins are still underdeveloped. This paper provides a scoping review and conceptual integration of 76 peer-reviewed studies (2000–2025) that define the Digital Twin as both a technological system and a cyber-physical entity. Using the PRISMA-ScR methodology, the review identifies key gaps in knowledge, governance, and inclusivity across three intersecting areas—conceptual/theoretical, empirical/technical, and policy/ethical research. Six themes emerged: trust and transparency, cultural inclusivity, data agency, participatory governance, AI personalization, and moral regulation. Building on these findings, the study proposes a three-dimensional framework that redefines the patient concept in the Digital Twin era along the axes of identity, agency, and participation, and adaptive governance. This framework underscores that ethical AI in healthcare requires reflexive, participatory, and equitable governance models that can evolve alongside technology. By viewing the Digital Twin as a socio-technical ecosystem rather than just a computational tool, this paper promotes a policy approach for responsible, transparent, and human-centered innovation in the field of intelligent medicine. Digital Twin AI in Healthcare Adaptive Governance Ethical AI Data Agency Patient Representation Algorithmic Accountability Participatory Health Systems Figures Figure 1 Figure 2 1. Introduction What is currently taking place in healthcare is a fundamental transformation of its structure, driven by progress in the biomedical field and the growing power of AI and other digital technologies. This is no ordinary process of technological innovation. It represents a transformation in the basic unit of healthcare analysis—the patient. Historically, the patient has been conceptualized as the passive recipient in the caregiving dynamic, situated within a healthcare hierarchy in which authority, knowledge, and decision-making were largely held by professionals. Data flow was largely one-way: from patients to healthcare providers, within episodic care restricted to encounters in healthcare settings. 1 Today, this paradigm shift is migrating toward a “decentralized, interactive, and data-intensive healthcare paradigm,” fueled by the adoption of AI-enabled technologies. The use of machine learning, natural language processing, predictive analytics, and AI-enabled decision support systems enables immediate data analysis, risk modeling, and adaptive treatment recommendations. When implemented on digital health platforms, wearables, and telemonitoring systems, these technologies make health an “endless conversation” between one’s physiology and intelligent systems. At the core of this paradigm shift is the development of the Digital Twin. This is an active, algorithmic, and constantly evolving virtual representation of the patient. 2 In contrast to conventional records, which are static impressions of the patient’s physical state, the Digital Twin integrates multimodal information, including activity logs, sensor data, genomic characterization, and environmental factors, to create a dynamic virtual model of the patient. 3 In this model, the patient assumes the virtual status of a cyber-physical system—a hybrid intelligence developed through the collaboration of human input and algorithms. However, this paradigm calls into question the ontology of the healthcare system. The healthcare provider is no longer the definitive diagnostician but becomes an interpreter within a collaborative ecosystem involving humans and machines. Clinical decision-making becomes more distributed, participatory, and anticipatory. 4 Digital Twins provide the simulation medicine necessary to virtually assess treatment plans before implementation on the biological system through the process of "simulation medicine." Nevertheless, this process raises significant ethical and policy concerns. As AI mediates visibility and presence, trust, traditionally built on human interaction, now relies on infrastructure, which must be grounded in fairness, transparency, and accountability on an algorithmic basis. 5 The instruments for trust in medicine are increasingly comprised of algorithmic audits, bias-detection systems, and ethical certifications for AI. However, there is a growing threat of inequity in computational terms. For there to be equity, therefore, there must be more to it than the provision of connectivity. There must be inclusive data governance, and Open AI systems and Digital Twin solutions that reflect diverse physiological and cultural realities. Cultural competence needs to move from a people skill to a systems principle. 6 Further, the advent of the Digital Twin requires that governance itself be reconfigured. Patients must not be viewed simply as sources of data but rather as actors in governance, influencing the use, protection, and development of their digital selves. 7 Government policy needs to move from regulating devices to regulating data and consent in real time. Research needs to expand from devices to examine the implications of this mediated identity on autonomy, responsibility, and ethics. In the end, what the Digital Twin heralds is, therefore, less about an overhaul of the healthcare industry itself or about medical innovations, per se, and more about the very notion of what it means to be a patient. The old patient was a biological entity to be cared for, whereas the new patient, as someone living in the age of AI, is nothing short of a pure intelligence—a co-producer, as it were, of their own well-being. This calls for, instead, a new kind of adaptability about healthcare itself. 8 1.1Gap in the Literature Despite rapid advancements in digital health, AI diagnostics, and precision medicine, a fundamental conceptual gap remains in how these technologies jointly alter the understanding of the patient and healthcare systems. While research frequently focuses on applications like predictive models, analytics, wearable sensors, and decision-support tools, most treat these as additions to traditional care rather than as forces that redefine healthcare itself. 9 The Digital Twin, a computational model that reflects and simulates the patient in real time, has gained increasing interest both technically and medically. 10 However, it remains conceptually fragmented and underdeveloped. Literature usually describes the Digital Twin mainly as a data integration or simulation tool, often missing its deeper implications for identity, agency, and governance. Therefore, there's an urgent need to clarify how the Digital Twin operates not just as a technology but as a new healthcare paradigm—one that unites biomedical, ethical, and policy aspects into an adaptable framework. This gap hampers policymakers and practitioners from creating effective governance for AI-driven care and understanding the ethical issues related to digital embodiment in healthcare. 1.2 Aim and Scope of the Study This study aims to define the Digital Twin framework in healthcare through a scoping review and qualitative thematic analysis, laying a structured theoretical foundation for its social, ethical, and policy aspects. It specifically seeks to: 1. Examine how the Digital Twin concept is described across healthcare, biomedical engineering, and AI ethics literature. 2. Trace its conceptual development—from a technical system optimization model to a broader view of patient identity and agency. 3. Highlight key themes on how Digital Twin technologies affect patient-system relationships, governance, and innovation policies. 4. Develop a conceptual model integrating identity, patient agency, and adaptive governance within the Digital Twin framework. By combining scoping literature mapping with conceptual synthesis, this research offers a comprehensive view of the Digital Twin as both a technological and a socio-ethical entity, bridging computational modeling and healthcare philosophy. 1.3 Conceptual Foundations: Understanding the Digital Twin in Healthcare The Digital Twin (DT) represents a major shift in healthcare thinking—from focusing on disease treatment to simulating overall health. Originally developed in engineering to create virtual models of physical systems, it now functions as a digital counterpart to the human body, constantly learning from real-time biological, behavioral, and environmental data. 11 In healthcare, the Digital Twin is a two-way intelligence system that connects the physical patient with its digital model through ongoing loops of sensing, analysis, and adjustment. 12 Unlike traditional static health records, it adapts dynamically to the individual, enabling continuous health monitoring, predictive insights, and virtual testing of various treatments. 1.3.1 Core Pillars AI-Driven Analytics – prediction, simulation, and optimization of health trajectories. 13 Sensor Integration – real-time data from wearables, IoT, and imaging systems. 14 Personalized Simulation Models – virtual experimentation with interventions before clinical application. 15 These pillars transform healthcare into a dynamic, data-driven ecosystem where interventions are optimized in silico before being applied in vivo . 1.4 From Engineering to Ethics Transformed from machines to humans, the Digital Twin evolves beyond a health model; it becomes a representation of humanity. It combines technical accuracy with ethical accountability, redefining the patient as a hybrid intelligence—part biological, part computational. 16 The rest of this paper develops this framework through three key areas: identity formation, agency and participation, and governance and policymaking. This sets the stage for a Digital Twin ecosystem in healthcare that is adaptive, transparent, and fair. 2. Methods: Scoping Review and Conceptual Framework Development To systematically frame the conceptual emergence and application of the Digital Twin (DT) within healthcare, a scoping review was conducted in accordance with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. 17 The primary aim of this review was to identify and synthesize key conceptual foundations, evidence sources, and knowledge gaps in the literature surrounding the Digital Twin as an evolving model for patient representation, simulation-based medicine, and data-driven healthcare systems. This review mapped theoretical, empirical, and policy-oriented discussions across multiple disciplinary intersections—specifically: 1. The transformation of healthcare systems toward AI-driven and data-centric models, 2. The evolution of digital health technologies and simulation infrastructures, 3. The theoretical construction of the Digital Twin as a socio-technical and biomedical construct, and 4. The ethical, policy, and governance implications of Digital Twin deployment in clinical and public health contexts. By identifying these intersections, the review provides the conceptual foundation to redefine the patient as an adaptive, computationally represented, and participatory entity within the socio-technical ecosystem of modern healthcare. Objectives of the Review This scoping review serves two overarching objectives: 1. To identify and classify the conceptual constructs that define the Digital Twin within healthcare—covering its epistemological origins, theoretical foundations, and how it differs from traditional biomedical and digital health models. 2. Additionally, to examine how the Digital Twin paradigm has reshaped perceptions of patient identity, agency, and system-level governance in AI-mediated healthcare settings. Building upon the evidence map and thematic analysis, this section introduces a conceptual framework that views the Digital Twin as both a technical system and an ethical concept in healthcare. Based on the literature reviewed, the patient is considered an adaptive, data-driven system whose identity, agency, and governance are constantly reshaped through algorithmic interactions. This framework sees the Digital Twin not just as a tool for simulation and prediction, but to reconceptualize patienthood in the era of intelligent medicine. 2.1 Search Strategy and Inclusion Criteria The search began with a guiding question: How has the concept of the Digital Twin evolved in healthcare, and what theoretical, empirical, and policy frameworks have developed around it? To answer this question, a comprehensive scoping review was conducted following PRISMA-ScR guidelines to explore both the technical and philosophical aspects of the Digital Twin in healthcare systems. From September to December 2025, the author systematically mapped the literature across four major academic databases—Scopus, Web of Science, PubMed, and Google Scholar—covering publications from 2000 to 2025. The search strategy used a structured combination of Boolean operators to ensure coverage across conceptual, empirical, and governance perspectives. Key search strings included: · “Digital Twin” AND “healthcare systems.” · “AI simulation” AND “patient representation.” · “Digital health” AND “virtual patient model.” · “Data governance” AND “algorithmic accountability.” · “Personalized medicine” AND “simulation modeling.” The author used an iterative search and charting method, screening results while adjusting keywords to achieve a balance between broad inclusion and focused concepts. The initial search found 1,218 records across different databases: 460 from Scopus, 352 from Web of Science, 260 from PubMed, and 146 from Google Scholar. After removing 158 duplicates, 1,060 unique records moved on to title and abstract screening. During this stage, 842 records were excluded for one or more of the following reasons: · Technical engineering studies not connected to healthcare or governance (n = 310) · AI or IoT implementation papers without a theoretical framework of the Digital Twin (n = 284) · Studies outside healthcare sectors such as manufacturing and aerospace (n = 155) · Non-peer-reviewed or gray literature (n = 93) The remaining 218 full-text articles were retrieved for detailed review. Each article was evaluated using predefined inclusion criteria: (a) explicit reference to the Digital Twin concept in a healthcare or biomedical context; (b) conceptual or empirical focus on AI-enabled simulation or digital patient representation; and (c) relevance to governance, ethics, or policy frameworks in data-driven care. At the full-text stage, 142 papers were excluded, primarily for insufficient engagement with the Digital Twin concept (n = 54), lack of policy or ethical relevance (n = 48), or overly descriptive technical reporting without systemic framing (n = 40). The final corpus included 76 peer-reviewed publications, which were subsequently categorized into three major domains: · 28 conceptual and theoretical papers exploring the ontology and epistemology of Digital Twins in healthcare; · 30 empirical studies focusing on AI-enabled simulation, digital patient modeling, and implementation frameworks; and · 18 policy and ethics reviews addressing governance, consent, data ownership, and algorithmic transparency. This structured, visual process laid the empirical groundwork for the conceptual framework discussed in the next sections. The PRISMA flow diagram (Figure 1) illustrates the search, screening, and inclusion process for the scoping review. 3. Result 3.1 Overview of the Literature The final dataset comprised 76 peer-reviewed publications from 2000 to 2024, highlighting a rapidly growing yet still fragmented body of research on the Digital Twin (DT) in healthcare. The literature is divided into three areas—conceptual/theoretical (n = 28), empirical/technical (n = 30), and policy/ethical (n = 18)—as shown in Table 1 . Conceptual research explores the ontological and epistemological aspects of the DT (Digital Twin), describing it as a continually learning digital representation of the patient and discussing its effects on knowledge, identity, and agency. 18 These studies, frequently grounded in philosophy of technology, systems theory, and bioethics, view the DT as progressing from static observation to predictive simulation in medicine. Empirical and technical studies, mainly in biomedical engineering and clinical informatics, focus on designing and validating prototypes in areas such as cardiology, oncology, and critical care. 19 However, these often lack integration with ethical or policy viewpoints, highlighting a gap between technological progress and governance readiness. Policy and ethical analyses stress the importance of adaptive regulation, algorithmic transparency, and patient involvement in AI-driven care. 20 Overall, the literature presents a highly interdisciplinary yet fragmented landscape—spanning informatics, bioethics, and systems design—pointing to the need for a unified conceptual framework that positions the Digital Twin as both a technical and ethical element in healthcare transformation. Table 1 Data Extraction and Coding Framework (n = 76) Category Analytical Focus Representative Methods Key Insights and Thematic Trends Conceptual / Theoretical (n = 28) Exploration of the ontology, epistemology, and system logic of the Digital Twin in healthcare. Conceptual analysis, systems thinking, critical discourse analysis, and philosophical inquiry. Defines the Digital Twin as a continuously learning digital counterpart; interrogates patient identity, data embodiment, and algorithmic epistemology; calls for new theoretical frameworks linking technology and ethics. 21 Empirical / Technical (n = 30) Development and validation of AI-enabled simulation models and digital patient representations in clinical domains. Computational modeling, case studies, prototype testing, simulation analysis, AI architecture design. Demonstrates feasibility in cardiology, oncology, orthopaedics, and ICU monitoring; emphasizes interoperability and predictive accuracy; reveals weak linkage between technological advancement and governance or ethics. 22 Policy / Ethical (n = 18) Examination of governance frameworks , data ethics , and regulatory design for AI-mediated healthcare systems. Policy analysis, normative ethics, comparative governance review, stakeholder mapping. Advocates for adaptive, participatory governance; introduces concepts of dynamic consent, algorithmic transparency, and patient co-governance; identifies gaps in data ownership and accountability standards. 23 3.2 Linking Literature Review to Thematic Results The reviewed corpus reveals a diverse yet uneven research landscape surrounding the Digital Twin in healthcare—ranging from conceptual explorations of its philosophical foundations to empirical model-building and emerging governance analyses. While this interdisciplinarity reflects the field’s richness, it also exposes fragmentation among technical innovation, ethical reflection, and policy coherence. The literature underscores that advancing Digital Twin research requires integrating human factors, data ethics, and system governance within a unified framework. From this synthesis, six cross-cutting themes emerge as critical to understanding how Digital Twins reconfigure patienthood and healthcare systems: (1) trust, transparency, and literacy; (2) cultural and demographic inclusivity; (3) digital self-efficacy and agency; (4) participatory governance and co-design; (5) personalization and predictive simulation; and (6) ethical and regulatory adaptation. Together, these themes trace the contours of an evolving ecosystem where patients, technologies, and institutions co-produce the future of intelligent healthcare. 1. Trust, Transparency, and Digital Literacy Many sources emphasize that trust in AI-enabled systems is essential for adopting Digital Twins. The perceived integrity, explainability, and dependability of algorithms influence user confidence and participation. 24 Digital health literacy—the ability of patients to interpret and utilize data insights—was shown to be critical for effective collaboration within Digital Twin ecosystems. 25 Without sufficient literacy and transparency, the cooperation between humans and machines may remain unequal. 2. Cultural Competence and Socio-Demographic Inclusivity The review indicates that factors such as ethnicity, gender, socioeconomic status, and age significantly affect patient engagement and data representation. Because Digital Twins depend on large, diverse datasets, underrepresentation of specific groups can lead to biased algorithms. 26 This underscores the importance of designing culturally sensitive frameworks and ensuring equitable data governance to prevent the reinforcement of systemic disparities. 3. Digital Self-Efficacy and Data Agency Studies highlight that patient confidence and independence in managing personal data are vital for participation in Digital Twins. The model assumes patients are digitally capable—able to interact with health simulations, modify parameters, and interpret predictions. However, gaps in digital readiness, especially among vulnerable groups, demonstrate the need for capacity-building and user-friendly interfaces to promote equitable access. 27 4. Advocacy, Co-Design, and Governance Participation The literature suggests that patients are moving from passive recipients to active stakeholders in innovation governance. They are increasingly involved in co-designing, validating, and overseeing their digital representations. 28 This participatory approach expands patient roles to governance agents who influence ethical standards, consent protocols, and algorithmic accountability, fostering shared responsibility among citizens, clinicians, and technology. 5. AI Personalization and Predictive Simulation Integrating Digital Twin systems into precision medicine emphasizes individual biological differences and predictive modeling. 29 Digital Twins are viewed as extensions of personalized care, enabling virtual simulation of treatments and disease trajectories before clinical application. 30 While this improves diagnostic accuracy, it also raises ethical concerns regarding equal access, data ownership, and oversight of simulated interventions. 6. Ethical, Regulatory, and Ontological Implications Creating digital patient identities through continuous data replication raises complex privacy, ownership, surveillance, and accountability issues. 31 Scholars note that Digital Twins blur the traditional lines between data subjects and systems, requiring adaptable regulations that acknowledge the hybrid nature of AI-supported patients—part biological, part digital, and part institutional. 32 Ethical governance must evolve from compliance to reflexive systems capable of auditing algorithms, ensuring fairness, and safeguarding data rights in real time. Box 1. Thematic Domains and Representative Insights on Digital Twin Implementation and Governance Thematic Domain Representative Insights from Literature Implications for the Digital Twin Framework 1. Trust, Transparency, and Digital Literacy Trust in AI-driven infrastructures is crucial for adoption, as user understanding of how algorithms make decisions boosts perceived reliability. 33 However, low digital health literacy limits patients' meaningful participation. 34 Create explainable and transparent Digital Twin systems featuring intuitive patient interfaces and educational tools to build trust and promote informed decision-making engagement. 2. Cultural Competence and Socio-Demographic Inclusivity Engagement varies across ethnicities, ages, and socioeconomic groups; insufficient data on these factors can cause algorithmic bias and unfair results. 35 Incorporate equity-by-design principles and promote diverse data representation during DT model training and assessment. Collaborating with communities through co-design improves cultural sensitivity. 3. Digital Self-Efficacy and Data Agency Patients’ confidence in using digital tools is linked to their autonomy and sense of control, yet many users struggle to interpret data or adjust privacy settings effectively. 36 Enhance patient agency by implementing dynamic consent mechanisms, intuitive dashboards, and educating users about data rights within DT interfaces. 4. Advocacy, Co-Design, and Governance Participation The patient’s role is evolving from a data subject to a stakeholder. 37 Using participatory design and advocacy approaches enhances the ethical validity and acceptance of digital health systems. 38 Establish patient representation in governance through co-design committees, algorithmic ethics boards, and participatory assessments of DT systems. 5. AI Personalization and Predictive Simulation DTs enable simulation-based medicine and personalized care, but they also pose risks of over-personalization and data commodification if ethical safeguards are not in place. 39 Implement governance protocols for simulations—covering ethical oversight, model validation, bias assessment, and ensuring equitable access to personalized care benefits. 6. Ethical, Regulatory, and Ontological Implications The development of digital patient identities raises ongoing concerns about data ownership, consent, surveillance, and accountability. 40 Develop advanced adaptive regulatory frameworks that incorporate continuous auditing, ensure transparent AI reporting, and include safeguards to prevent over-surveillance or dehumanization of patients. The six thematic domains in Box 1 reveal that the Digital Twin in healthcare transcends a mere technological instrument, with important ethical, social, and governance dimensions. A common theme in the literature is that trust and transparency are preconditions for adoption, equity and agency determine inclusion, and participation in governance is necessary for legitimacy. These combined messages provide the groundwork for the Digital Twin Framework for Personalized Healthcare Systems on ethical design, algorithmic accountability, and participatory policymaking for the responsible advancement of AI-driven healthcare. 3.3 Conceptual Framework: Viewing the Digital Twin as a Socio-Technical System in Healthcare The integration of AI, data infrastructure, and simulation technologies in healthcare marks a paradigm shift: the emergence of the Digital Twin (DT) as a new representation of the patient. No longer a static biomedical subject, the patient now exists as a cyber-physical entity—a continuously learning, data-producing, and decision-influencing agent within a distributed ecosystem of algorithms, devices, and human expertise. 41 Drawing on insights from the scoping review, this conceptual framework articulates how the Digital Twin reconfigures the healthcare landscape across three interdependent dimensions: (1) Identity Construction, (2) Agency and Participation, and (3) Governance and Policy Design. Together, these dimensions form the foundation for a Digital Twin Framework for Personalized Healthcare Systems, visualized in Fig. 2 , where the technical architecture (data acquisition, modeling, simulation) is inseparable from its ethical and governance architecture (trust, transparency, and inclusion). As illustrated in Table 2 , the Digital Twin features a multi-layered architecture that combines technical accuracy with ethical oversight. The Data Acquisition Layer gathers continuous, multimodal data from IoT sensors, wearables, and electronic health records, all in compliance with strict consent and provenance regulations. The Integration and Processing Layer merges these diverse datasets using federated learning and edge computing, maintaining privacy and data minimization standards. In the Modeling and Simulation Layer, predictive and personalized models are created via agent-based and twin-driven simulations, which require transparency in algorithms and fairness audits. The Decision Intelligence Layer converts analytical results into adaptive, explainable clinical insights, with ongoing human supervision. Lastly, the Governance and Trust Layer protects system integrity through blockchain audit logs, algorithm registries, and formal ethics and regulatory reviews. These layers collectively form a socio-technical ecosystem where technological progress, ethical responsibility, and patient trust are interconnected. Table 2 Layered Architecture Overview Layer Description Key Technologies Ethical & Policy Dimensions Data Acquisition Layer Collects continuous multimodal data IoT sensors, wearables, EHR systems Data provenance, consent management Integration & Processing Layer Harmonizes structured and unstructured datasets Federated learning, edge computing Privacy preservation, data minimization Modeling & Simulation Layer Builds predictive and personalized models Digital twins, agent-based modeling Algorithmic transparency, fairness auditing Decision Intelligence Layer Provides adaptive insights and prescriptive actions Reinforcement learning, explainable AI Human oversight, accountability Governance & Trust Layer Manages oversight and compliance Blockchain audit trails, algorithm registries Ethics review, regulatory conformance 3.4 Identity Construction: Evolving from Biological Subject to Digital Entity In traditional biomedical paradigms, patient identity was defined by the body—its symptoms, laboratory readings, and episodic clinical interactions. 42 The introduction of the Digital Twin transforms this ontology: the patient’s “self” becomes continuously instantiated in digital form, reconstructed through multimodal data including physiological signals, genetic information, behavioral patterns, and environmental variables. 43 The Digital Twin thus represents not merely a digital record, but a living simulation—an adaptive model capable of forecasting health trajectories, testing interventions, and learning from outcomes. This computational embodiment allows medicine to shift from reactive diagnosis to predictive and preventive simulation medicine. 44 However, this evolution raises profound ontological and ethical questions. The patient’s identity becomes distributed across data systems, creating risks of commodification, fragmentation, and surveillance. 45 Innovation policy must therefore recognize that the Digital Twin is both a representation and a participant—an algorithmic actor co-constituted by data infrastructures, AI systems, and institutional governance. 3.5 Agency and Participation: The Patient as a Co-Intelligent System Actor As healthcare becomes increasingly digitized, the patient is no longer a passive endpoint of care but a dynamic agent within the Digital Twin ecosystem. Patients engage continuously with their digital counterparts through dashboards, wearable interfaces, and AI-assisted platforms, shaping treatment trajectories in real time. 46 This represents the rise of the AI-mediated, participatory patient—one who co-produces insights, contributes to model learning, and exercises decision-making power through data interaction. 47 However, this agency is asymmetrically distributed. Factors such as digital literacy, device access, and socio-economic context determine a patient’s ability to engage effectively with their digital twin. 48 The concept of “data agency”—the capacity to understand, control, and act upon one’s own health data—emerges as central to equitable participation. 49 Our framework proposes that Digital Twins should be designed for co-intelligence, embedding user interfaces that enhance comprehension, interpretability, and consent literacy. Patients must be recognized not only as data contributors but as co-creators of knowledge, shaping how models evolve and respond within the larger digital ecosystem. 3.6 Governance and Policy Design: Shifting Focus from mere Compliance to Collaborative Co-Governance The proliferation of Digital Twins in healthcare exposes critical tensions in existing regulatory systems, which remain anchored to static models of consent, privacy, and accountability. Since Digital Twins operate through ongoing data transfer and algorithm updates, traditional compliance frameworks—designed for periodic interventions—are insufficient to address their complexity. 50 The Digital Twin paradigm calls for adaptive and participatory governance built on three core principles: Dynamic Consent: enabling patients to modify permissions and data-sharing preferences in real time. 51 Algorithmic Transparency: mandating auditability and explainability for predictive models that influence care; 52 Ethical Co-Design: embedding patient representation in the governance of platforms, datasets, and innovation standards. 53 Regulatory logic must thus shift from protectionism to co-governance, where patients, clinicians, developers, and regulators collaboratively oversee the evolution of digital healthcare systems. 54 Policy design must address risks unique to the Digital Twin ecosystem—algorithmic bias, surveillance capitalism, data sovereignty, and exclusion from datasets—while safeguarding equity and trust as infrastructural conditions of care. 4. Policy Implications and Innovation Strategies Translating the Digital Twin framework from concept to implementation requires reorienting health policy around three interconnected areas: regulation, equity, and infrastructure. These domains create the policy structure needed to maintain trust, fairness, and patient engagement in AI-driven healthcare systems. 55 As healthcare becomes a learning and adaptive system, governance must evolve from static compliance to models that enable continuous oversight, participatory governance, and ethical reflection. 56 This change involves new regulatory tools and a deliberate integration of equity principles and participatory design into digital infrastructure. Box 2 outlines the policy and innovation strategies that support this shift, detailing the main goals, key actions, and expected system outcomes for each domain. The three pathways—Regulatory Redesign, Equity-by-Design Innovation, and Patient-Centric Infrastructure Development—offer a roadmap for creating ethical, transparent, and inclusive Digital Twin ecosystems in healthcare. Box 2. Policy and Innovation Pathways for the Digital Twin Ecosystem in Healthcare Policy Domain Core Objective Key Policy Mechanisms / Interventions Expected System-Level Outcomes 1. Regulatory Redesign: From Static Compliance to Adaptive Governance Coordinating health data regulation with the real-time, dynamic functioning of Digital Twin systems. • Establish dynamic consent protocols that allow patients to update their data-sharing preferences. Additionally, it requires algorithmic auditability and real-time risk assessment monitoring. • Establish cross-sector interoperability standards for DT data exchange and ensure patient representation in regulatory and ethics review boards. • Improved clarity and openness in accountability. • Lower algorithmic opacity and address compliance delays will lead to increased public trust and better patient engagement in AI-driven healthcare. 2. Equity-by-Design Innovation: Embedding Inclusion in Digital Health Development Ensuring that Digital Twin technologies are designed and deployed to prevent digital exclusion and algorithmic bias. • Require developers to showcase inclusive and participatory design approaches. • Fund community-based digital literacy initiatives and capacity-building efforts. • Implement equity and bias audit requirements for all AI-driven healthcare systems.• encourage the use of open and diverse data sources for training models. • Greater representation of marginalized groups in health data. • Reduction of systemic bias and inequality in digital health outcomes. • Improved social legitimacy of Digital Twin systems. 3. Patient-Centric Infrastructure Development: Building Participatory Health Ecosystems Developing transparent, modular, and participatory infrastructures to facilitate the integration of Digital Twins within healthcare systems. • Develop national or regional Digital Twin data commons governed by ethical and transparent structures. • Integrate patient feedback loops into AI model updates and clinical workflows. • Promote open-source and interoperable architectures through public-private partnerships. • Encourage governance sandboxes to enable safe testing and innovation. • Enhanced collaboration in producing healthcare knowledge. • Digital systems that are scalable, interoperable, and governed by ethical ecosystems. • Institutionalizing participatory governance in digital health policy. The transition toward a Digital Twin–enabled healthcare ecosystem demands a policy transformation that matches its technological and ethical complexity. Traditional governance models—rooted in static compliance, institutional control, and episodic oversight—are no longer adequate for systems that evolve autonomously through continuous data exchange and machine learning. 57 Policy frameworks must therefore shift toward adaptive governance, characterized by flexibility, real-time accountability, and participatory oversight. 58 This includes the development of dynamic consent protocols that evolve with patient preferences and context, algorithmic transparency and auditability to ensure explainability and trust, and data interoperability standards that allow individuals to retain agency over their digital identities across platforms. Regulation in the Digital Twin era must move from controlling technology to co-evolving with it, embedding patient participation within ethical and regulatory review processes. 59 Yet governance cannot function effectively without equity. The Digital Twin promises hyper-personalized care but risks deepening disparities if inclusion is not deliberately built into its design. Equity-by-design must become a foundational principle of innovation, requiring diverse data representation, participatory co-design with marginalized communities, and pre-deployment equity and bias audits to prevent algorithmic exclusion. 60 At the same time, the Digital Twin transforms healthcare infrastructure into more than just isolated data storage; it creates modular, participatory ecosystems that promote shared data stewardship, transparency, and continuous learning. 61 Governments and institutions should therefore invest in national or regional Digital Twin commons governed ethically and openly, integrate patient feedback loops into algorithmic updates and clinical decision-making, and encourage open-source, interoperable architectures to foster public trust and shared accountability. Finally, this transformation must be embedded within national innovation strategies, aligning health, science, technology, and ethics policy through coordinated funding for responsible AI research, cross-ministerial task forces on digital governance, and incentives for open innovation ecosystems that prioritize societal benefit over narrow commercial gain. Together, these policy pathways—adaptive regulation, equity-by-design, participatory infrastructure, and strategic alignment—constitute the governance scaffolding of the Digital Twin era, ensuring that the pursuit of predictive, personalized, and intelligent medicine remains firmly grounded in ethical accountability, inclusivity, and human-centered innovation. 5. Discussion The emergence of the Digital Twin (DT) signifies a significant shift in healthcare—moving from simply adopting new technologies to redesigning health systems as intelligent, adaptive ecosystems. Within this framework, the patient, the system, and the algorithm function as interconnected nodes within a constantly evolving network of data, decisions, and feedback. This section consolidates the review’s key conceptual insights across five areas: (1) epistemology and knowledge creation, (2) trust and accountability, (3) equity and inclusion, (4) governance innovation, and (5) alignment of global systems. 5.1 From Representation to Co-Creation: Developing a New Epistemology of Health The Digital Twin challenges traditional medicine’s understanding, which has historically relied on static models such as anatomy and biostatistics. 62 By allowing continuous data collection and simulation, it develops a dynamic, predictive, and reflexive approach to knowledge. Health is seen not as a fixed condition but as a constantly evolving system that can be modeled, tested, and improved in real time. 63 Therefore, the Digital Twin serves as both a source and a tool for generating knowledge, with patients, clinicians, and algorithms collaborating to produce insights. This transformation requires new frameworks for algorithmic epistemology that combine computational inference with ethical considerations and offer clarity on how learning systems validate evidence and uphold scientific standards. 5.2 Trust, Accountability, and the Infrastructure of Care In healthcare with DT support, trust moves from individual relationships to the underlying infrastructure. Its sustainability depends on transparent algorithms, reliable data, and well-defined decision processes 64 Patients need to understand not just what the system observes but also how it interprets information and makes decisions. Achieving this mutual transparency involves tools such as algorithmic audits, explainable AI interfaces, and public registries of certified models. 65 As a result, trust is built through openness and collective accountability, rather than just institutional reputation. 5.3 Equity, Representation, and the Politics of Data The Digital Twin enables precise and personalized approaches but may also deepen data-driven inequalities if inclusivity isn't prioritized. Representation is inherently political—shaping whose data defines what is considered 'normal' and whose health outcomes are predictable. 66 To avoid computational exclusion, equity-by-design should be incorporated throughout: diversifying datasets, supporting participatory co-design with marginalized groups, and conducting bias audits before deployment. 67 In this view, the DT acts as a platform for data justice, promoting fairness and inclusion as essential, structural elements of digital health innovation. 5.4 Governance Innovation: From Institutional Control to Co-Evolutionary Systems Traditional health governance tends to be linear and reactive, whereas digital technology functions as a co-evolving, real-time system. Therefore, effective oversight must be adaptive, iterative, and participatory. Important reforms include implementing dynamic consent—enabling patients to update their permissions—along with ongoing algorithmic audits and involving patients and ethicists in review processes. 68 This co-governance approach redefines legitimacy as arising from collaboration, with humans and algorithms sharing accountability within a reflexive regulatory framework. 5.5 Global System Alignment: Standardization and Ethics of Scale The effectiveness of Digital Twin systems hinges on worldwide interoperability and ethical consistency. Fragmented regulations and isolated data structures currently restrict scalability and trust. International efforts should concentrate on establishing interoperability standards aligned with ISO/IEC and WHO frameworks, creating transnational governance for genomic and behavioral data, and building open infrastructures that honor sovereignty and ensure fair access. Global health governance must also ensure that technical standards develop hand-in-hand with ethical principles, safeguarding human dignity in data-driven care. 69 5.6 Integrative Synthesis: The Digital Twin as an Ethical-Technical Ecosystem In the rapidly evolving field of healthcare AI, the Digital Twin goes beyond being a simple diagnostic tool; it serves as an ethical and technical ecosystem that transforms the roles of patients and participants in healthcare. From an epistemological perspective, it turns medicine into a continually evolving learning system. 70 Ethically, it requires transparency, fairness, and accountability by design. 71 Politically, it shifts authority between human expertise and algorithmic intelligence. 72 Systemically, it demands governance capable of learning and adapting alongside technology. 73 As illustrated in Fig. 2 , the Digital Twin Framework for Personalized Healthcare Systems shows that innovation is inherently linked to ethics, and efficiency to equity. The sustainability of this approach relies on developing trustworthy, participatory, and inclusive digital embodiment systems that uphold human values in an age dominated by algorithms. 6. Future Directions To encourage responsible development, future research and policy efforts should prioritize: 1. Empirical validation of Digital Twin applications across diverse healthcare settings. 2. Ethical foresight studies concerning identity, autonomy, and the moral implications of digital embodiments. 3. Policy experiments across different sectors to test adaptable governance frameworks. 4. Transdisciplinary collaboration among AI experts, healthcare professionals, ethicists, and patient advocates. Only through these comprehensive efforts can the Digital Twin transition from a data-driven innovation to a transformative force in human well-being—a fusion of biology, technology, and ethics that redefines healthcare. 7. Limitations This conceptual and scoping review offers a thorough overview of the Digital Twin paradigm in healthcare, but several limitations should be noted. First, the body of literature is still developing and uneven, with more theoretical and engineering-focused studies than empirical research in clinical settings. Second, there is conceptual diversity and ambiguity—where “Digital Twin” is used to mean data visualization, simulation, or complete system embodiment 74 —hindering clear terminology and comparison. Third, most studies come from high-income, technologically advanced countries, introducing geographic and socio-economic biases that limit broader applicability. Lastly, since this framework remains mainly conceptual, more empirical and participatory research is necessary to validate governance and ethical models in actual healthcare systems. Despite these limitations, the review provides a foundational perspective for understanding and applying the Digital Twin as a socio-technical, moral, and policy framework. 8. Conclusion The emergence of the Digital Twin in healthcare represents more than just a technological advancement; it signifies a fundamental shift in the philosophy of medicine. It redefines the patient as a hybrid intelligence—an evolving entity integrated into ongoing cycles of data collection, algorithmic analysis, and clinical decision-making. 75 This shift collapses traditional boundaries between body and data, individual and system, observation and simulation. Philosophically, the Digital Twin challenges the ontology of personhood by introducing a computational self that is simultaneously material and virtual, private and networked. Ethically, it demands a reconfiguration of trust, transparency, and accountability, ensuring that digital embodiment serves human autonomy rather than eroding it. Policy-wise, it calls for a move from compliance to co-governance, embedding patients within adaptive frameworks of dynamic consent, equity-by-design innovation, and participatory oversight. In essence, the Digital Twin marks the dawn of a new epistemic and moral frontier in healthcare — where prediction becomes prevention, data becomes dialogue, and the patient becomes a partner in the simulation of life itself. 76 The challenge ahead is not only to design smarter systems but also to ensure they remain human-centered, ethically grounded, and democratically governed in this unfolding era of digital embodiment. Declarations Acknowledgments Anusha Das Gupta for formatting the tables, figures, and boxes. Author Contributions Conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing, visualization, supervision, reviewing. Conflicts of Interest The author declare no conflicts of interest. Funding: Not applicable References Alsabah, M., Abdulrazzaq, M., Albahri, N. A. S. & Albahri, O. S. 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A comprehensive review of digital twin in healthcare in the scope of simulative. (2025) doi:10.1177/20552076241304078. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8511797","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570337850,"identity":"eff61378-7e4f-43a4-a360-a2d5eb018906","order_by":0,"name":"Atantra Das Gupta","email":"data:image/png;base64,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","orcid":"","institution":"Management Development Institute","correspondingAuthor":true,"prefix":"","firstName":"Atantra","middleName":"Das","lastName":"Gupta","suffix":""}],"badges":[],"createdAt":"2026-01-04 09:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8511797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8511797/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100005314,"identity":"da150eaa-8ed4-4a23-9772-20ff529fb0b0","added_by":"auto","created_at":"2026-01-12 05:33:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":681398,"visible":true,"origin":"","legend":"\u003cp\u003eScoping Review Flow Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8511797/v1/318207e85383489f86749ada.png"},{"id":100005315,"identity":"a1935fbc-755a-4a01-afdc-3899deb3d14d","added_by":"auto","created_at":"2026-01-12 05:33:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":629323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArchitecture and Governance of the Digital Twin for Personalized Healthcare\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8511797/v1/2a7e0242a6ea1114a283925a.png"},{"id":100406291,"identity":"b7892a5b-8942-49f1-82d6-e3859c4d71a0","added_by":"auto","created_at":"2026-01-16 12:59:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3217239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8511797/v1/ee0c58bc-2b7a-4626-900c-d2ef096ab074.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Redefining the Patient in the Digital Twin Age: A Scoping and Conceptual Model for Flexible, Ethical, and Inclusive Healthcare Systems ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWhat is currently taking place in healthcare is a fundamental transformation of its structure, driven by progress in the biomedical field and the growing power of AI and other digital technologies. This is no ordinary process of technological innovation. It represents a transformation in the basic unit of healthcare analysis—the patient.\u003c/p\u003e\n\u003cp\u003eHistorically, the patient has been conceptualized as the passive recipient in the caregiving dynamic, situated within a healthcare hierarchy in which authority, knowledge, and decision-making were largely held by professionals. Data flow was largely one-way: from patients to healthcare providers, within episodic care restricted to encounters in healthcare settings.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eToday, this paradigm shift is migrating toward a “decentralized, interactive, and data-intensive healthcare paradigm,” fueled by the adoption of AI-enabled technologies. The use of machine learning, natural language processing, predictive analytics, and AI-enabled decision support systems enables immediate data analysis, risk modeling, and adaptive treatment recommendations. When implemented on digital health platforms, wearables, and telemonitoring systems, these technologies make health an “endless conversation” between one’s physiology and intelligent systems.\u003c/p\u003e\n\u003cp\u003eAt the core of this paradigm shift is the development of the Digital Twin. This is an active, algorithmic, and constantly evolving virtual representation of the patient.\u003csup\u003e2\u003c/sup\u003e In contrast to conventional records, which are static impressions of the patient’s physical state, the Digital Twin integrates multimodal information, including activity logs, sensor data, genomic characterization, and environmental factors, to create a dynamic virtual model of the patient.\u003csup\u003e3\u003c/sup\u003e In this model, the patient assumes the virtual status of a cyber-physical system—a hybrid intelligence developed through the collaboration of human input and algorithms.\u003c/p\u003e\n\u003cp\u003eHowever, this paradigm calls into question the ontology of the healthcare system. The healthcare provider is no longer the definitive diagnostician but becomes an interpreter within a collaborative ecosystem involving humans and machines. Clinical decision-making becomes more distributed, participatory, and anticipatory.\u003csup\u003e4\u003c/sup\u003e Digital Twins provide the simulation medicine necessary to virtually assess treatment plans before implementation on the biological system through the process of \"simulation medicine.\"\u003c/p\u003e\n\u003cp\u003eNevertheless, this process raises significant ethical and policy concerns. As AI mediates visibility and presence, trust, traditionally built on human interaction, now relies on infrastructure, which must be grounded in fairness, transparency, and accountability on an algorithmic basis.\u003csup\u003e5\u003c/sup\u003e The instruments for trust in medicine are increasingly comprised of algorithmic audits, bias-detection systems, and ethical certifications for AI. However, there is a growing threat of inequity in computational terms.\u003c/p\u003e\n\u003cp\u003eFor there to be equity, therefore, there must be more to it than the provision of connectivity. There must be inclusive data governance, and Open AI systems and Digital Twin solutions that reflect diverse physiological and cultural realities. Cultural competence needs to move from a people skill to a systems principle.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFurther, the advent of the Digital Twin requires that governance itself be reconfigured. Patients must not be viewed simply as sources of data but rather as actors in governance, influencing the use, protection, and development of their digital selves.\u003csup\u003e7\u003c/sup\u003e Government policy needs to move from regulating devices to regulating data and consent in real time. Research needs to expand from devices to examine the implications of this mediated identity on autonomy, responsibility, and ethics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the end, what the Digital Twin heralds is, therefore, less about an overhaul of the healthcare industry itself or about medical innovations, per se, and more about the very notion of what it means to be a patient. The old patient was a biological entity to be cared for, whereas the new patient, as someone living in the age of AI, is nothing short of a pure intelligence—a co-producer, as it were, of their own well-being. This calls for, instead, a new kind of adaptability about healthcare itself.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1Gap in the Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite rapid advancements in digital health, AI diagnostics, and precision medicine, a fundamental conceptual gap remains in how these technologies jointly alter the understanding of the patient and healthcare systems. While research frequently focuses on applications like predictive models, analytics, wearable sensors, and decision-support tools, most treat these as additions to traditional care rather than as forces that redefine healthcare itself.\u003csup\u003e9\u003c/sup\u003e The Digital Twin, a computational model that reflects and simulates the patient in real time, has gained increasing interest both technically and medically.\u003csup\u003e10\u003c/sup\u003e However, it remains conceptually fragmented and underdeveloped. Literature usually describes the Digital Twin mainly as a data integration or simulation tool, often missing its deeper implications for identity, agency, and governance. Therefore, there's an urgent need to clarify how the Digital Twin operates not just as a technology but as a new healthcare paradigm—one that unites biomedical, ethical, and policy aspects into an adaptable framework.\u003cbr\u003e\u0026nbsp;This gap hampers policymakers and practitioners from creating effective governance for AI-driven care and understanding the ethical issues related to digital embodiment in healthcare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Aim and Scope of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to define the Digital Twin framework in healthcare through a scoping review and qualitative thematic analysis, laying a structured theoretical foundation for its social, ethical, and policy aspects. It specifically seeks to:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Examine how the Digital Twin concept is described across healthcare, biomedical engineering, and AI ethics literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Trace its conceptual development—from a technical system optimization model to a broader view of patient identity and agency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Highlight key themes on how Digital Twin technologies affect patient-system relationships, governance, and innovation policies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. Develop a conceptual model integrating identity, patient agency, and adaptive governance within the Digital Twin framework. By combining scoping literature mapping with conceptual synthesis, this research offers a comprehensive view of the Digital Twin as both a technological and a socio-ethical entity, bridging computational modeling and healthcare philosophy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Conceptual Foundations: Understanding the Digital Twin in Healthcare\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Digital Twin (DT) represents a major shift in healthcare thinking—from focusing on disease treatment to simulating overall health. Originally developed in engineering to create virtual models of physical systems, it now functions as a digital counterpart to the human body, constantly learning from real-time biological, behavioral, and environmental data.\u003csup\u003e11\u003c/sup\u003e In healthcare, the Digital Twin is a two-way intelligence system that connects the physical patient with its digital model through ongoing loops of sensing, analysis, and adjustment.\u003csup\u003e12\u003c/sup\u003e Unlike traditional static health records, it adapts dynamically to the individual, enabling continuous health monitoring, predictive insights, and virtual testing of various treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.1 Core Pillars\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eAI-Driven Analytics – prediction, simulation, and optimization of health trajectories.\u003csup\u003e13\u003c/sup\u003e\u003c/li\u003e\n \u003cli\u003eSensor Integration – real-time data from wearables, IoT, and imaging systems.\u003csup\u003e14\u003c/sup\u003e\u003c/li\u003e\n \u003cli\u003ePersonalized Simulation Models – virtual experimentation with interventions before clinical application.\u003csup\u003e15\u003c/sup\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThese pillars transform healthcare into a dynamic, data-driven ecosystem where interventions are optimized \u003cem\u003ein silico\u003c/em\u003e before being applied \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 From Engineering to Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransformed from machines to humans, the Digital Twin evolves beyond a health model; it becomes a representation of humanity. It combines technical accuracy with ethical accountability, redefining the patient as a hybrid intelligence—part biological, part computational.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe rest of this paper develops this framework through three key areas: identity formation, agency and participation, and governance and policymaking. This sets the stage for a Digital Twin ecosystem in healthcare that is adaptive, transparent, and fair.\u003c/p\u003e"},{"header":"2. Methods: Scoping Review and Conceptual Framework Development","content":"\u003cp\u003eTo systematically frame the conceptual emergence and application of the Digital Twin (DT) within healthcare, a scoping review was conducted in accordance with the \u003cem\u003ePRISMA Extension for Scoping Reviews (PRISMA-ScR)\u003c/em\u003e guidelines.\u003csup\u003e17\u003c/sup\u003e\u003cbr\u003e\u0026nbsp;The primary aim of this review was to identify and synthesize key conceptual foundations, evidence sources, and knowledge gaps in the literature surrounding the Digital Twin as an evolving model for patient representation, simulation-based medicine, and data-driven healthcare systems.\u003c/p\u003e\n\u003cp\u003eThis review mapped theoretical, empirical, and policy-oriented discussions across multiple disciplinary intersections\u0026mdash;specifically:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;The transformation of healthcare systems toward AI-driven and data-centric models,\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;The evolution of digital health technologies and simulation infrastructures,\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;The theoretical construction of the Digital Twin as a socio-technical and biomedical construct, and\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;The ethical, policy, and governance implications of Digital Twin deployment in clinical and public health contexts.\u003c/p\u003e\n\u003cp\u003eBy identifying these intersections, the review provides the conceptual foundation to redefine the patient as an adaptive, computationally represented, and participatory entity within the socio-technical ecosystem of modern healthcare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives of the Review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis scoping review serves two overarching objectives:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTo identify and classify the conceptual constructs that define the Digital Twin within healthcare\u0026mdash;covering its epistemological origins, theoretical foundations, and how it differs from traditional biomedical and digital health models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Additionally, to examine how the Digital Twin paradigm has reshaped perceptions of patient identity, agency, and system-level governance in AI-mediated healthcare settings.\u003c/p\u003e\n\u003cp\u003eBuilding upon the evidence map and thematic analysis, this section introduces a conceptual framework that views the Digital Twin as both a technical system and an ethical concept in healthcare. Based on the literature reviewed, the patient is considered an adaptive, data-driven system whose identity, agency, and governance are constantly reshaped through algorithmic interactions. This framework sees the Digital Twin not just as a tool for simulation and prediction, but to reconceptualize patienthood in the era of intelligent medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Search Strategy and Inclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search began with a guiding question: How has the concept of the Digital Twin evolved in healthcare, and what theoretical, empirical, and policy frameworks have developed around it?\u003cbr\u003e\u0026nbsp;To answer this question, a comprehensive scoping review was conducted following PRISMA-ScR guidelines to explore both the technical and philosophical aspects of the Digital Twin in healthcare systems.\u003c/p\u003e\n\u003cp\u003eFrom September to December 2025, the author systematically mapped the literature across four major academic databases\u0026mdash;Scopus, Web of Science, PubMed, and Google Scholar\u0026mdash;covering publications from 2000 to 2025. The search strategy used a structured combination of Boolean operators to ensure coverage across conceptual, empirical, and governance perspectives. Key search strings included:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026ldquo;Digital Twin\u0026rdquo; AND \u0026ldquo;healthcare systems.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026ldquo;AI simulation\u0026rdquo; AND \u0026ldquo;patient representation.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026ldquo;Digital health\u0026rdquo; AND \u0026ldquo;virtual patient model.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026ldquo;Data governance\u0026rdquo; AND \u0026ldquo;algorithmic accountability.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026ldquo;Personalized medicine\u0026rdquo; AND \u0026ldquo;simulation modeling.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe author used an iterative search and charting method, screening results while adjusting keywords to achieve a balance between broad inclusion and focused concepts. The initial search found 1,218 records across different databases: 460 from Scopus, 352 from Web of Science, 260 from PubMed, and 146 from Google Scholar. After removing 158 duplicates, 1,060 unique records moved on to title and abstract screening.\u003c/p\u003e\n\u003cp\u003eDuring this stage, 842 records were excluded for one or more of the following reasons:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026nbsp;Technical engineering studies not connected to healthcare or governance (n = 310)\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026nbsp;AI or IoT implementation papers without a theoretical framework of the Digital Twin (n = 284)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Studies outside healthcare sectors such as manufacturing and aerospace (n = 155)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Non-peer-reviewed or gray literature (n = 93)\u003c/p\u003e\n\u003cp\u003eThe remaining 218 full-text articles were retrieved for detailed review. Each article was evaluated using predefined inclusion criteria:\u003cbr\u003e\u0026nbsp;(a) explicit reference to the Digital Twin concept in a healthcare or biomedical context;\u003cbr\u003e\u0026nbsp;(b) conceptual or empirical focus on AI-enabled simulation or digital patient representation; and\u003cbr\u003e\u0026nbsp;(c) relevance to governance, ethics, or policy frameworks in data-driven care. At the full-text stage, 142 papers were excluded, primarily for insufficient engagement with the Digital Twin concept (n = 54), lack of policy or ethical relevance (n = 48), or overly descriptive technical reporting without systemic framing (n = 40).\u003c/p\u003e\n\u003cp\u003eThe final corpus included 76 peer-reviewed publications, which were subsequently categorized into three major domains:\u003c/p\u003e\n\u003cp\u003e\u0026middot; 28 conceptual and theoretical papers exploring the ontology and epistemology of Digital Twins in healthcare;\u003c/p\u003e\n\u003cp\u003e\u0026middot; 30 empirical studies focusing on AI-enabled simulation, digital patient modeling, and implementation frameworks; and\u003c/p\u003e\n\u003cp\u003e\u0026middot; 18 policy and ethics reviews addressing governance, consent, data ownership, and algorithmic transparency.\u003c/p\u003e\n\u003cp\u003eThis structured, visual process laid the empirical groundwork for the conceptual framework discussed in the next sections.\u003c/p\u003e\n\u003cp\u003eThe PRISMA flow diagram (Figure 1) illustrates the search, screening, and inclusion process for the scoping review.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overview of the Literature\u003c/h2\u003e \u003cp\u003eThe final dataset comprised 76 peer-reviewed publications from 2000 to 2024, highlighting a rapidly growing yet still fragmented body of research on the Digital Twin (DT) in healthcare. The literature is divided into three areas\u0026mdash;conceptual/theoretical (n\u0026thinsp;=\u0026thinsp;28), empirical/technical (n\u0026thinsp;=\u0026thinsp;30), and policy/ethical (n\u0026thinsp;=\u0026thinsp;18)\u0026mdash;as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Conceptual research explores the ontological and epistemological aspects of the DT (Digital Twin), describing it as a continually learning digital representation of the patient and discussing its effects on knowledge, identity, and agency. \u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese studies, frequently grounded in philosophy of technology, systems theory, and bioethics, view the DT as progressing from static observation to predictive simulation in medicine.\u003c/p\u003e \u003cp\u003eEmpirical and technical studies, mainly in biomedical engineering and clinical informatics, focus on designing and validating prototypes in areas such as cardiology, oncology, and critical care.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, these often lack integration with ethical or policy viewpoints, highlighting a gap between technological progress and governance readiness.\u003c/p\u003e \u003cp\u003ePolicy and ethical analyses stress the importance of adaptive regulation, algorithmic transparency, and patient involvement in AI-driven care.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOverall, the literature presents a highly interdisciplinary yet fragmented landscape\u0026mdash;spanning informatics, bioethics, and systems design\u0026mdash;pointing to the need for a unified conceptual framework that positions the Digital Twin as both a technical and ethical element in healthcare transformation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Extraction and Coding Framework (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalytical Focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresentative Methods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey Insights and Thematic Trends\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConceptual / Theoretical (n\u0026thinsp;=\u0026thinsp;28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExploration of the \u003cb\u003eontology, epistemology, and system logic\u003c/b\u003e of the Digital Twin in healthcare.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConceptual analysis, systems thinking, critical discourse analysis, and philosophical inquiry.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefines the Digital Twin as a continuously learning digital counterpart; interrogates patient identity, data embodiment, and algorithmic epistemology; calls for new theoretical frameworks linking technology and ethics.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmpirical / Technical (n\u0026thinsp;=\u0026thinsp;30)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment and validation of \u003cb\u003eAI-enabled simulation models\u003c/b\u003e and \u003cb\u003edigital patient representations\u003c/b\u003e in clinical domains.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComputational modeling, case studies, prototype testing, simulation analysis, AI architecture design.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDemonstrates feasibility in cardiology, oncology, orthopaedics, and ICU monitoring; emphasizes interoperability and predictive accuracy; reveals weak linkage between technological advancement and governance or ethics.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolicy / Ethical (n\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExamination of \u003cb\u003egovernance frameworks\u003c/b\u003e, \u003cb\u003edata ethics\u003c/b\u003e, and \u003cb\u003eregulatory design\u003c/b\u003e for AI-mediated healthcare systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy analysis, normative ethics, comparative governance review, stakeholder mapping.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdvocates for adaptive, participatory governance; introduces concepts of dynamic consent, algorithmic transparency, and patient co-governance; identifies gaps in data ownership and accountability standards.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Linking Literature Review to Thematic Results\u003c/h2\u003e \u003cp\u003eThe reviewed corpus reveals a diverse yet uneven research landscape surrounding the Digital Twin in healthcare\u0026mdash;ranging from conceptual explorations of its philosophical foundations to empirical model-building and emerging governance analyses. While this interdisciplinarity reflects the field\u0026rsquo;s richness, it also exposes fragmentation among technical innovation, ethical reflection, and policy coherence. The literature underscores that advancing Digital Twin research requires integrating human factors, data ethics, and system governance within a unified framework. From this synthesis, six cross-cutting themes emerge as critical to understanding how Digital Twins reconfigure patienthood and healthcare systems: (1) trust, transparency, and literacy; (2) cultural and demographic inclusivity; (3) digital self-efficacy and agency; (4) participatory governance and co-design; (5) personalization and predictive simulation; and (6) ethical and regulatory adaptation. Together, these themes trace the contours of an evolving ecosystem where patients, technologies, and institutions co-produce the future of intelligent healthcare.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1. Trust, Transparency, and Digital Literacy\u003c/h3\u003e\n\u003cp\u003eMany sources emphasize that trust in AI-enabled systems is essential for adopting Digital Twins. The perceived integrity, explainability, and dependability of algorithms influence user confidence and participation.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Digital health literacy\u0026mdash;the ability of patients to interpret and utilize data insights\u0026mdash;was shown to be critical for effective collaboration within Digital Twin ecosystems.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Without sufficient literacy and transparency, the cooperation between humans and machines may remain unequal.\u003c/p\u003e\n\u003ch3\u003e2. Cultural Competence and Socio-Demographic Inclusivity\u003c/h3\u003e\n\u003cp\u003eThe review indicates that factors such as ethnicity, gender, socioeconomic status, and age significantly affect patient engagement and data representation. Because Digital Twins depend on large, diverse datasets, underrepresentation of specific groups can lead to biased algorithms.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e This underscores the importance of designing culturally sensitive frameworks and ensuring equitable data governance to prevent the reinforcement of systemic disparities.\u003c/p\u003e\n\u003ch3\u003e3. Digital Self-Efficacy and Data Agency\u003c/h3\u003e\n\u003cp\u003eStudies highlight that patient confidence and independence in managing personal data are vital for participation in Digital Twins. The model assumes patients are digitally capable\u0026mdash;able to interact with health simulations, modify parameters, and interpret predictions. However, gaps in digital readiness, especially among vulnerable groups, demonstrate the need for capacity-building and user-friendly interfaces to promote equitable access.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e4. Advocacy, Co-Design, and Governance Participation\u003c/h3\u003e\n\u003cp\u003eThe literature suggests that patients are moving from passive recipients to active stakeholders in innovation governance. They are increasingly involved in co-designing, validating, and overseeing their digital representations.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis participatory approach expands patient roles to governance agents who influence ethical standards, consent protocols, and algorithmic accountability, fostering shared responsibility among citizens, clinicians, and technology.\u003c/p\u003e\n\u003ch3\u003e5. AI Personalization and Predictive Simulation\u003c/h3\u003e\n\u003cp\u003eIntegrating Digital Twin systems into precision medicine emphasizes individual biological differences and predictive modeling.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Digital Twins are viewed as extensions of personalized care, enabling virtual simulation of treatments and disease trajectories before clinical application.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e While this improves diagnostic accuracy, it also raises ethical concerns regarding equal access, data ownership, and oversight of simulated interventions.\u003c/p\u003e\n\u003ch3\u003e6. Ethical, Regulatory, and Ontological Implications\u003c/h3\u003e\n\u003cp\u003eCreating digital patient identities through continuous data replication raises complex privacy, ownership, surveillance, and accountability issues.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eScholars note that Digital Twins blur the traditional lines between data subjects and systems, requiring adaptable regulations that acknowledge the hybrid nature of AI-supported patients\u0026mdash;part biological, part digital, and part institutional.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEthical governance must evolve from compliance to reflexive systems capable of auditing algorithms, ensuring fairness, and safeguarding data rights in real time.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBox 1. Thematic Domains and Representative Insights on Digital Twin Implementation and Governance\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThematic Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepresentative Insights from Literature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImplications for the Digital Twin Framework\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Trust, Transparency, and Digital Literacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust in AI-driven infrastructures is crucial for adoption, as user understanding of how algorithms make decisions boosts perceived reliability.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e However, low digital health literacy limits patients' meaningful participation.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCreate explainable and transparent Digital Twin systems featuring intuitive patient interfaces and educational tools to build trust and promote informed decision-making engagement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Cultural Competence and Socio-Demographic Inclusivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngagement varies across ethnicities, ages, and socioeconomic groups; insufficient data on these factors can cause algorithmic bias and unfair results.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncorporate equity-by-design principles and promote diverse data representation during DT model training and assessment. Collaborating with communities through co-design improves cultural sensitivity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Digital Self-Efficacy and Data Agency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients\u0026rsquo; confidence in using digital tools is linked to their autonomy and sense of control, yet many users struggle to interpret data or adjust privacy settings effectively.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnhance patient agency by implementing dynamic consent mechanisms, intuitive dashboards, and educating users about data rights within DT interfaces.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Advocacy, Co-Design, and Governance Participation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe patient\u0026rsquo;s role is evolving from a data subject to a stakeholder.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Using participatory design and advocacy approaches enhances the ethical validity and acceptance of digital health systems.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstablish patient representation in governance through co-design committees, algorithmic ethics boards, and participatory assessments of DT systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. AI Personalization and Predictive Simulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTs enable simulation-based medicine and personalized care, but they also pose risks of over-personalization and data commodification if ethical safeguards are not in place.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImplement governance protocols for simulations\u0026mdash;covering ethical oversight, model validation, bias assessment, and ensuring equitable access to personalized care benefits.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Ethical, Regulatory, and Ontological Implications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe development of digital patient identities raises ongoing concerns about data ownership, consent, surveillance, and accountability.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevelop advanced adaptive regulatory frameworks that incorporate continuous auditing, ensure transparent AI reporting, and include safeguards to prevent over-surveillance or dehumanization of patients.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe six thematic domains in Box 1 reveal that the Digital Twin in healthcare transcends a mere technological instrument, with important ethical, social, and governance dimensions. A common theme in the literature is that trust and transparency are preconditions for adoption, equity and agency determine inclusion, and participation in governance is necessary for legitimacy. These combined messages provide the groundwork for the Digital Twin Framework for Personalized Healthcare Systems on ethical design, algorithmic accountability, and participatory policymaking for the responsible advancement of AI-driven healthcare.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Conceptual Framework: Viewing the Digital Twin as a Socio-Technical System in Healthcare\u003c/h2\u003e \u003cp\u003eThe integration of AI, data infrastructure, and simulation technologies in healthcare marks a paradigm shift: the emergence of the Digital Twin (DT) as a new representation of the patient.\u003c/p\u003e \u003cp\u003eNo longer a static biomedical subject, the patient now exists as a cyber-physical entity\u0026mdash;a continuously learning, data-producing, and decision-influencing agent within a distributed ecosystem of algorithms, devices, and human expertise.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDrawing on insights from the scoping review, this conceptual framework articulates how the Digital Twin reconfigures the healthcare landscape across three interdependent dimensions:\u003c/p\u003e \u003cp\u003e(1) Identity Construction, (2) Agency and Participation, and (3) Governance and Policy Design.\u003c/p\u003e \u003cp\u003eTogether, these dimensions form the foundation for a Digital Twin Framework for Personalized Healthcare Systems, visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where the technical architecture (data acquisition, modeling, simulation) is inseparable from its ethical and governance architecture (trust, transparency, and inclusion).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Digital Twin features a multi-layered architecture that combines technical accuracy with ethical oversight. The Data Acquisition Layer gathers continuous, multimodal data from IoT sensors, wearables, and electronic health records, all in compliance with strict consent and provenance regulations.\u003c/p\u003e \u003cp\u003eThe Integration and Processing Layer merges these diverse datasets using federated learning and edge computing, maintaining privacy and data minimization standards.\u003c/p\u003e \u003cp\u003eIn the Modeling and Simulation Layer, predictive and personalized models are created via agent-based and twin-driven simulations, which require transparency in algorithms and fairness audits.\u003c/p\u003e \u003cp\u003eThe Decision Intelligence Layer converts analytical results into adaptive, explainable clinical insights, with ongoing human supervision.\u003c/p\u003e \u003cp\u003eLastly, the Governance and Trust Layer protects system integrity through blockchain audit logs, algorithm registries, and formal ethics and regulatory reviews. These layers collectively form a socio-technical ecosystem where technological progress, ethical responsibility, and patient trust are interconnected.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLayered Architecture Overview\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLayer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Technologies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEthical \u0026amp; Policy Dimensions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData Acquisition Layer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollects continuous multimodal data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIoT sensors, wearables, EHR systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData provenance, consent management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntegration \u0026amp; Processing Layer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarmonizes structured and unstructured datasets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFederated learning, edge computing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrivacy preservation, data minimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModeling \u0026amp; Simulation Layer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilds predictive and personalized models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital twins, agent-based modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlgorithmic transparency, fairness auditing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDecision Intelligence Layer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvides adaptive insights and prescriptive actions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReinforcement learning, explainable AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman oversight, accountability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGovernance \u0026amp; Trust Layer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManages oversight and compliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlockchain audit trails, algorithm registries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEthics review, regulatory conformance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Identity Construction: Evolving from Biological Subject to Digital Entity\u003c/h2\u003e \u003cp\u003eIn traditional biomedical paradigms, patient identity was defined by the body\u0026mdash;its symptoms, laboratory readings, and episodic clinical interactions.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe introduction of the Digital Twin transforms this ontology: the patient\u0026rsquo;s \u0026ldquo;self\u0026rdquo; becomes continuously instantiated in digital form, reconstructed through multimodal data including physiological signals, genetic information, behavioral patterns, and environmental variables.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe Digital Twin thus represents not merely a digital record, but a living simulation\u0026mdash;an adaptive model capable of forecasting health trajectories, testing interventions, and learning from outcomes. This computational embodiment allows medicine to shift from reactive diagnosis to predictive and preventive simulation medicine.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, this evolution raises profound ontological and ethical questions. The patient\u0026rsquo;s identity becomes distributed across data systems, creating risks of commodification, fragmentation, and surveillance.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Innovation policy must therefore recognize that the Digital Twin is both a representation and a participant\u0026mdash;an algorithmic actor co-constituted by data infrastructures, AI systems, and institutional governance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Agency and Participation: The Patient as a Co-Intelligent System Actor\u003c/h2\u003e \u003cp\u003eAs healthcare becomes increasingly digitized, the patient is no longer a passive endpoint of care but a dynamic agent within the Digital Twin ecosystem.\u003c/p\u003e \u003cp\u003ePatients engage continuously with their digital counterparts through dashboards, wearable interfaces, and AI-assisted platforms, shaping treatment trajectories in real time.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis represents the rise of the AI-mediated, participatory patient\u0026mdash;one who co-produces insights, contributes to model learning, and exercises decision-making power through data interaction.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, this agency is asymmetrically distributed. Factors such as digital literacy, device access, and socio-economic context determine a patient\u0026rsquo;s ability to engage effectively with their digital twin. \u003csup\u003e48\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe concept of \u0026ldquo;data agency\u0026rdquo;\u0026mdash;the capacity to understand, control, and act upon one\u0026rsquo;s own health data\u0026mdash;emerges as central to equitable participation.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur framework proposes that Digital Twins should be designed for co-intelligence, embedding user interfaces that enhance comprehension, interpretability, and consent literacy. Patients must be recognized not only as data contributors but as co-creators of knowledge, shaping how models evolve and respond within the larger digital ecosystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Governance and Policy Design: Shifting Focus from mere Compliance to Collaborative Co-Governance\u003c/h2\u003e \u003cp\u003eThe proliferation of Digital Twins in healthcare exposes critical tensions in existing regulatory systems, which remain anchored to static models of consent, privacy, and accountability.\u003c/p\u003e \u003cp\u003eSince Digital Twins operate through ongoing data transfer and algorithm updates, traditional compliance frameworks\u0026mdash;designed for periodic interventions\u0026mdash;are insufficient to address their complexity.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe Digital Twin paradigm calls for adaptive and participatory governance built on three core principles:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDynamic Consent: enabling patients to modify permissions and data-sharing preferences in real time.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAlgorithmic Transparency: mandating auditability and explainability for predictive models that influence care;\u003csup\u003e52\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEthical Co-Design: embedding patient representation in the governance of platforms, datasets, and innovation standards.\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eRegulatory logic must thus shift from protectionism to co-governance, where patients, clinicians, developers, and regulators collaboratively oversee the evolution of digital healthcare systems.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePolicy design must address risks unique to the Digital Twin ecosystem\u0026mdash;algorithmic bias, surveillance capitalism, data sovereignty, and exclusion from datasets\u0026mdash;while safeguarding equity and trust as infrastructural conditions of care.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Policy Implications and Innovation Strategies","content":"\u003cp\u003eTranslating the Digital Twin framework from concept to implementation requires reorienting health policy around three interconnected areas: regulation, equity, and infrastructure. These domains create the policy structure needed to maintain trust, fairness, and patient engagement in AI-driven healthcare systems.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e As healthcare becomes a learning and adaptive system, governance must evolve from static compliance to models that enable continuous oversight, participatory governance, and ethical reflection.\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e This change involves new regulatory tools and a deliberate integration of equity principles and participatory design into digital infrastructure. Box 2 outlines the policy and innovation strategies that support this shift, detailing the main goals, key actions, and expected system outcomes for each domain. The three pathways\u0026mdash;Regulatory Redesign, Equity-by-Design Innovation, and Patient-Centric Infrastructure Development\u0026mdash;offer a roadmap for creating ethical, transparent, and inclusive Digital Twin ecosystems in healthcare.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBox 2. Policy and Innovation Pathways for the Digital Twin Ecosystem in Healthcare\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Objective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Policy Mechanisms / Interventions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected System-Level Outcomes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Regulatory Redesign: From Static Compliance to Adaptive Governance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinating health data regulation with the real-time, dynamic functioning of Digital Twin systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Establish dynamic consent protocols that allow patients to update their data-sharing preferences. Additionally, it requires algorithmic auditability and real-time risk assessment monitoring. \u0026bull; Establish cross-sector interoperability standards for DT data exchange and ensure patient representation in regulatory and ethics review boards.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Improved clarity and openness in accountability.\u003c/p\u003e \u003cp\u003e\u0026bull; Lower algorithmic opacity and address compliance delays will lead to increased public trust and better patient engagement in AI-driven healthcare.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Equity-by-Design Innovation: Embedding Inclusion in Digital Health Development\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnsuring that Digital Twin technologies are designed and deployed to prevent digital exclusion and algorithmic bias.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Require developers to showcase inclusive and participatory design approaches. \u0026bull; Fund community-based digital literacy initiatives and capacity-building efforts. \u0026bull; Implement equity and bias audit requirements for all AI-driven healthcare systems.\u0026bull; encourage the use of open and diverse data sources for training models.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Greater representation of marginalized groups in health data. \u0026bull; Reduction of systemic bias and inequality in digital health outcomes. \u0026bull; Improved social legitimacy of Digital Twin systems.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Patient-Centric Infrastructure Development: Building Participatory Health Ecosystems\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeveloping transparent, modular, and participatory infrastructures to facilitate the integration of Digital Twins within healthcare systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Develop \u003cem\u003enational or regional Digital Twin data commons\u003c/em\u003e governed by ethical and transparent structures. \u0026bull; Integrate \u003cem\u003epatient feedback loops\u003c/em\u003e into AI model updates and clinical workflows.\u003c/p\u003e \u003cp\u003e\u0026bull; Promote \u003cem\u003eopen-source and interoperable architectures\u003c/em\u003e through public-private partnerships.\u003c/p\u003e \u003cp\u003e\u0026bull; Encourage governance sandboxes to enable safe testing and innovation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Enhanced collaboration in producing healthcare knowledge.\u003c/p\u003e \u003cp\u003e\u0026bull; Digital systems that are scalable, interoperable, and governed by ethical ecosystems.\u003c/p\u003e \u003cp\u003e\u0026bull; Institutionalizing participatory governance in digital health policy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe transition toward a Digital Twin\u0026ndash;enabled healthcare ecosystem demands a policy transformation that matches its technological and ethical complexity. Traditional governance models\u0026mdash;rooted in static compliance, institutional control, and episodic oversight\u0026mdash;are no longer adequate for systems that evolve autonomously through continuous data exchange and machine learning.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e Policy frameworks must therefore shift toward adaptive governance, characterized by flexibility, real-time accountability, and participatory oversight.\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e This includes the development of dynamic consent protocols that evolve with patient preferences and context, algorithmic transparency and auditability to ensure explainability and trust, and data interoperability standards that allow individuals to retain agency over their digital identities across platforms.\u003c/p\u003e \u003cp\u003eRegulation in the Digital Twin era must move from controlling technology to co-evolving with it, embedding patient participation within ethical and regulatory review processes.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e Yet governance cannot function effectively without equity. The Digital Twin promises hyper-personalized care but risks deepening disparities if inclusion is not deliberately built into its design. Equity-by-design must become a foundational principle of innovation, requiring diverse data representation, participatory co-design with marginalized communities, and pre-deployment equity and bias audits to prevent algorithmic exclusion.\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAt the same time, the Digital Twin transforms healthcare infrastructure into more than just isolated data storage; it creates modular, participatory ecosystems that promote shared data stewardship, transparency, and continuous learning.\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e Governments and institutions should therefore invest in national or regional Digital Twin commons governed ethically and openly, integrate patient feedback loops into algorithmic updates and clinical decision-making, and encourage open-source, interoperable architectures to foster public trust and shared accountability. Finally, this transformation must be embedded within national innovation strategies, aligning health, science, technology, and ethics policy through coordinated funding for responsible AI research, cross-ministerial task forces on digital governance, and incentives for open innovation ecosystems that prioritize societal benefit over narrow commercial gain. Together, these policy pathways\u0026mdash;adaptive regulation, equity-by-design, participatory infrastructure, and strategic alignment\u0026mdash;constitute the governance scaffolding of the Digital Twin era, ensuring that the pursuit of predictive, personalized, and intelligent medicine remains firmly grounded in ethical accountability, inclusivity, and human-centered innovation.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe emergence of the Digital Twin (DT) signifies a significant shift in healthcare\u0026mdash;moving from simply adopting new technologies to redesigning health systems as intelligent, adaptive ecosystems. Within this framework, the patient, the system, and the algorithm function as interconnected nodes within a constantly evolving network of data, decisions, and feedback.\u003c/p\u003e \u003cp\u003eThis section consolidates the review\u0026rsquo;s key conceptual insights across five areas: (1) epistemology and knowledge creation, (2) trust and accountability, (3) equity and inclusion, (4) governance innovation, and (5) alignment of global systems.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 From Representation to Co-Creation: Developing a New Epistemology of Health\u003c/h2\u003e \u003cp\u003eThe Digital Twin challenges traditional medicine\u0026rsquo;s understanding, which has historically relied on static models such as anatomy and biostatistics.\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e By allowing continuous data collection and simulation, it develops a dynamic, predictive, and reflexive approach to knowledge. Health is seen not as a fixed condition but as a constantly evolving system that can be modeled, tested, and improved in real time.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e Therefore, the Digital Twin serves as both a source and a tool for generating knowledge, with patients, clinicians, and algorithms collaborating to produce insights.\u003c/p\u003e \u003cp\u003eThis transformation requires new frameworks for algorithmic epistemology that combine computational inference with ethical considerations and offer clarity on how learning systems validate evidence and uphold scientific standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Trust, Accountability, and the Infrastructure of Care\u003c/h2\u003e \u003cp\u003eIn healthcare with DT support, trust moves from individual relationships to the underlying infrastructure. Its sustainability depends on transparent algorithms, reliable data, and well-defined decision processes \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePatients need to understand not just what the system observes but also how it interprets information and makes decisions. Achieving this mutual transparency involves tools such as algorithmic audits, explainable AI interfaces, and public registries of certified models.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e As a result, trust is built through openness and collective accountability, rather than just institutional reputation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Equity, Representation, and the Politics of Data\u003c/h2\u003e \u003cp\u003eThe Digital Twin enables precise and personalized approaches but may also deepen data-driven inequalities if inclusivity isn't prioritized. Representation is inherently political\u0026mdash;shaping whose data defines what is considered 'normal' and whose health outcomes are predictable.\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo avoid computational exclusion, equity-by-design should be incorporated throughout: diversifying datasets, supporting participatory co-design with marginalized groups, and conducting bias audits before deployment.\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e In this view, the DT acts as a platform for data justice, promoting fairness and inclusion as essential, structural elements of digital health innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Governance Innovation: From Institutional Control to Co-Evolutionary Systems\u003c/h2\u003e \u003cp\u003eTraditional health governance tends to be linear and reactive, whereas digital technology functions as a co-evolving, real-time system. Therefore, effective oversight must be adaptive, iterative, and participatory. Important reforms include implementing dynamic consent\u0026mdash;enabling patients to update their permissions\u0026mdash;along with ongoing algorithmic audits and involving patients and ethicists in review processes.\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis co-governance approach redefines legitimacy as arising from collaboration, with humans and algorithms sharing accountability within a reflexive regulatory framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Global System Alignment: Standardization and Ethics of Scale\u003c/h2\u003e \u003cp\u003eThe effectiveness of Digital Twin systems hinges on worldwide interoperability and ethical consistency.\u003c/p\u003e \u003cp\u003eFragmented regulations and isolated data structures currently restrict scalability and trust.\u003c/p\u003e \u003cp\u003eInternational efforts should concentrate on establishing interoperability standards aligned with ISO/IEC and WHO frameworks, creating transnational governance for genomic and behavioral data, and building open infrastructures that honor sovereignty and ensure fair access.\u003c/p\u003e \u003cp\u003eGlobal health governance must also ensure that technical standards develop hand-in-hand with ethical principles, safeguarding human dignity in data-driven care.\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Integrative Synthesis: The Digital Twin as an Ethical-Technical Ecosystem\u003c/h2\u003e \u003cp\u003eIn the rapidly evolving field of healthcare AI, the Digital Twin goes beyond being a simple diagnostic tool; it serves as an ethical and technical ecosystem that transforms the roles of patients and participants in healthcare. From an epistemological perspective, it turns medicine into a continually evolving learning system.\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEthically, it requires transparency, fairness, and accountability by design.\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePolitically, it shifts authority between human expertise and algorithmic intelligence. \u003csup\u003e72\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSystemically, it demands governance capable of learning and adapting alongside technology. \u003csup\u003e73\u003c/sup\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Digital Twin Framework for Personalized Healthcare Systems shows that innovation is inherently linked to ethics, and efficiency to equity.\u003c/p\u003e \u003cp\u003eThe sustainability of this approach relies on developing trustworthy, participatory, and inclusive digital embodiment systems that uphold human values in an age dominated by algorithms.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Future Directions","content":"\u003cp\u003eTo encourage responsible development, future research and policy efforts should prioritize:\u003c/p\u003e\n\u003cp\u003e1. Empirical validation of Digital Twin applications across diverse healthcare settings.\u003c/p\u003e\n\u003cp\u003e2. Ethical foresight studies concerning identity, autonomy, and the moral implications of digital embodiments.\u003c/p\u003e\n\u003cp\u003e3. Policy experiments across different sectors to test adaptable governance frameworks.\u003c/p\u003e\n\u003cp\u003e4. Transdisciplinary collaboration among AI experts, healthcare professionals, ethicists, and patient advocates.\u003c/p\u003e\n\u003cp\u003eOnly through these comprehensive efforts can the Digital Twin transition from a data-driven innovation to a transformative force in human well-being\u0026mdash;a fusion of biology, technology, and ethics that redefines healthcare.\u003c/p\u003e"},{"header":"7. Limitations","content":"\u003cp\u003eThis conceptual and scoping review offers a thorough overview of the Digital Twin paradigm in healthcare, but several limitations should be noted. First, the body of literature is still developing and uneven, with more theoretical and engineering-focused studies than empirical research in clinical settings.\u003c/p\u003e \u003cp\u003eSecond, there is conceptual diversity and ambiguity\u0026mdash;where \u0026ldquo;Digital Twin\u0026rdquo; is used to mean data visualization, simulation, or complete system embodiment\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e\u0026mdash;hindering clear terminology and comparison.\u003c/p\u003e \u003cp\u003eThird, most studies come from high-income, technologically advanced countries, introducing geographic and socio-economic biases that limit broader applicability.\u003c/p\u003e \u003cp\u003eLastly, since this framework remains mainly conceptual, more empirical and participatory research is necessary to validate governance and ethical models in actual healthcare systems. Despite these limitations, the review provides a foundational perspective for understanding and applying the Digital Twin as a socio-technical, moral, and policy framework.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThe emergence of the Digital Twin in healthcare represents more than just a technological advancement; it signifies a fundamental shift in the philosophy of medicine. It redefines the patient as a hybrid intelligence\u0026mdash;an evolving entity integrated into ongoing cycles of data collection, algorithmic analysis, and clinical decision-making.\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis shift collapses traditional boundaries between body and data, individual and system, observation and simulation. Philosophically, the Digital Twin challenges the ontology of personhood by introducing a computational self that is simultaneously material and virtual, private and networked.\u003c/p\u003e \u003cp\u003eEthically, it demands a reconfiguration of trust, transparency, and accountability, ensuring that digital embodiment serves human autonomy rather than eroding it.\u003c/p\u003e \u003cp\u003ePolicy-wise, it calls for a move from compliance to co-governance, embedding patients within adaptive frameworks of dynamic consent, equity-by-design innovation, and participatory oversight. In essence, the Digital Twin marks the dawn of a new epistemic and moral frontier in healthcare \u0026mdash; where prediction becomes prevention, data becomes dialogue, and the patient becomes a partner in the simulation of life itself.\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe challenge ahead is not only to design smarter systems but also to ensure they remain human-centered, ethically grounded, and democratically governed in this unfolding era of digital embodiment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnusha Das Gupta for formatting the tables, figures, and boxes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, methodology, \u0026nbsp;validation, formal analysis, investigation, resources, data curation, writing, visualization, supervision, reviewing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: Not applicable\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlsabah, M., Abdulrazzaq, M., Albahri, N. 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(2025) doi:10.1177/20552076241304078.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Twin, AI in Healthcare, Adaptive Governance, Ethical AI, Data Agency, Patient Representation, Algorithmic Accountability, Participatory Health Systems","lastPublishedDoi":"10.21203/rs.3.rs-8511797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8511797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe emergence of the Digital Twin (DT) in healthcare signifies a revolutionary change in how patients are represented, managed, and governed within AI-driven systems. Traditionally, healthcare relied on episodic data and static diagnoses; now, the Digital Twin develops a dynamic, continuously learning digital replica of the patient\u0026mdash;merging biological identity, data infrastructure, and algorithmic analysis. Although increasingly important in precision medicine and clinical simulations, the theoretical foundations and ethical oversight of Digital Twins are still underdeveloped.\u003c/p\u003e \u003cp\u003eThis paper provides a scoping review and conceptual integration of 76 peer-reviewed studies (2000\u0026ndash;2025) that define the Digital Twin as both a technological system and a cyber-physical entity. Using the PRISMA-ScR methodology, the review identifies key gaps in knowledge, governance, and inclusivity across three intersecting areas\u0026mdash;conceptual/theoretical, empirical/technical, and policy/ethical research. Six themes emerged: trust and transparency, cultural inclusivity, data agency, participatory governance, AI personalization, and moral regulation.\u003c/p\u003e \u003cp\u003eBuilding on these findings, the study proposes a three-dimensional framework that redefines the patient concept in the Digital Twin era along the axes of identity, agency, and participation, and adaptive governance. This framework underscores that ethical AI in healthcare requires reflexive, participatory, and equitable governance models that can evolve alongside technology. By viewing the Digital Twin as a socio-technical ecosystem rather than just a computational tool, this paper promotes a policy approach for responsible, transparent, and human-centered innovation in the field of intelligent medicine.\u003c/p\u003e","manuscriptTitle":"Redefining the Patient in the Digital Twin Age: A Scoping and Conceptual Model for Flexible, Ethical, and Inclusive Healthcare Systems ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:33:16","doi":"10.21203/rs.3.rs-8511797/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd489b8f-6db0-4063-8f37-3b4c9eb47006","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T05:33:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:33:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8511797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8511797","identity":"rs-8511797","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}