A structured literature search was conducted across PubMed, Scopus, and Web of Science from database inception to March 2025 using the following query: (“digital twin” OR “digital twins”) AND (“multi-scale” OR “multi-omics” OR “systems biology” OR “hierarchical modeling”) AND (“personalized medicine” OR “precision medicine” OR “individualized healthcare”).

MSDTs embody the principle that health and disease processes unfold across interconnected biological layers, each governed by distinct but interdependent dynamics. Building robust MSDTs requires a deep understanding of these layers and their interactions.

Building a functional multi-scale digital twin (MSDT) first requires the definition of a coherent multi-level and multi-layer system model of the biological entity under study. Before considering data availability or fusion strategies, it is necessary to specify the relevant biological scales (molecular, cellular, tissue, organ, clinical, behavioral, environmental), their state variables, and the causal and functional interactions linking them (1, 18).

From a systems and network medicine perspective, this modeling step is foundational: without an explicit representation of cross-scale dependencies and feedback loops, subsequent data integration risks producing high-dimensional but mechanistically inconsistent models (22). Recent work in network medicine emphasizes that disease phenotypes emerge from perturbations of interconnected molecular and physiological networks, rather than from isolated components, reinforcing the need for structured, multi-layer modeling frameworks prior to data-driven learning (22).

More broadly, the theory of complex biological systems provides formal tools for constructing such hierarchical models, including multi-layer networks, dynamical systems, and stochastic processes that capture both within-scale dynamics and cross-scale coupling (18, 23). These approaches offer principled ways to represent emergence, nonlinearity, and system-level behavior, which are central to the objectives of MSDTs.

Only once this modeling scaffold is established does multi-modal data fusion become the next critical challenge. At that stage, heterogeneous data streams, such as genomics, proteomics, imaging, clinical variables, behavioral metrics, and environmental exposures, can be mapped onto the predefined model structure and integrated using early-, late-, or hybrid-fusion strategies (24, 25).

To manage these fused datasets, advanced artificial intelligence (AI) methods are essential. Graph Neural Networks (GNNs) have emerged as a particularly powerful tool for representing complex biological and clinical networks, where nodes can represent genes, tissues, comorbidities, or lifestyle factors, and edges capture functional relationships (26). GNNs allow the model to reason over relationships rather than isolated features, an essential capacity for multi-scale biological systems.

Beyond graph models, causal inference techniques are crucial. Traditional predictive models often capture correlations rather than causation, which limits their clinical utility for decision support. Structural causal models, counterfactual modeling, and potential outcome frameworks provide the mathematical foundation for predicting “what would happen if” scenarios, a central feature of effective MSDTs (21).

Furthermore, reinforcement learning (RL) plays a key role when MSDTs are used for sequential decision-making, such as optimizing chronic disease management over time. RL agents learn optimal treatment policies by interacting with the simulated twin environment, continuously updating strategies as patient data evolves (27). Particularly in dynamic and long-term conditions like diabetes or heart failure, RL-enabled twins could personalize therapeutic adjustments in real time.

Generative models such as Generative Adversarial Networks (GANs) and diffusion models further enrich MSDT capabilities. They can simulate synthetic yet realistic patient trajectories, predict rare adverse events, or augment sparse datasets by generating plausible unobserved scenarios (9). These generative approaches help bridge the gap between incomplete observational data and the full complexity of human health dynamics.

For dynamic simulation, multi-scale modeling frameworks are essential. Agent-based models represent entities like cells, immune responses, or patient behaviors as autonomous agents, capable of interacting with one another according to defined rules. These models are particularly valuable for studying emergent phenomena such as tumor microenvironment evolution or epidemic spread. Meanwhile, mechanistic multi-scale models integrate molecular dynamics (e.g., signaling pathways), organ function (e.g., cardiac electrophysiology), and systemic responses (e.g., hemodynamics), often using systems of differential equations.

Because biological systems are stochastic by nature, stochastic differential equations (SDEs) offer a mathematically rigorous way to capture random fluctuations in health status over time (28). For example, blood glucose dynamics in diabetes are influenced by intrinsic metabolic noise, environmental variability (e.g., meals, stress), and behavioral randomness (e.g., exercise), all of which can be elegantly captured using SDEs.

Ultimately, the technical architecture of MSDTs must balance three core imperatives: richness (capturing cross-scale interactions in high resolution), robustness (handling noise, missingness, and uncertainty), and interpretability (allowing clinicians to understand and trust the model outputs). Architectures that combine mechanistic knowledge (from biology and physics) with flexible AI components will likely outperform purely data-driven or purely theoretical models.

Building such systems requires not only technical innovation but also an interdisciplinary commitment to open data standards, scalable computational infrastructure, and clinical validation. Only through this integration of techniques and philosophies can multi-scale digital twins transition from conceptual promise to clinical reality, driving the future of personalized medicine.

The implementation of MSDTs unlocks transformative possibilities across a wide spectrum of clinical fields (Table 2). By integrating diverse biological, clinical, behavioral, and environmental data, MSDTs enable unprecedented personalization of diagnostics, prognostics, and therapeutics.

In personalized oncology, MSDTs can integrate genomic profiles (e.g., mutational burden, microsatellite instability) (29), transcriptomic signatures (e.g., immune infiltration markers), imaging phenotypes (e.g., radiomics), and treatment histories to simulate tumor evolution under various therapeutic strategies. By modeling both tumor-intrinsic factors and the tumor microenvironment, MSDTs can predict individual response trajectories to chemotherapy, immunotherapy, or targeted agents. For instance, a patient's likelihood of developing immune-related adverse events during checkpoint inhibitor therapy could be anticipated through twin simulations, allowing for proactive management strategies.

In cardiometabolic diseases, the multi-scale integration of genetic predispositions (e.g., lipid metabolism variants), lifestyle behaviors (e.g., diet, exercise), physiological parameters (e.g., blood pressure, glycemic control), and imaging data (e.g., coronary CT angiography) enables MSDTs to forecast disease progression and optimize intervention timing (8). For example, a digital twin of a patient at risk for atherosclerosis could simulate the impact of different lifestyle modifications, pharmacological treatments (e.g., statins, PCSK9 inhibitors), or procedural interventions (e.g., stenting) on future cardiovascular events. Furthermore, continuous real-time updates through wearable data can recalibrate risk predictions dynamically, enhancing secondary prevention strategies.

Nevertheless several challenges remain. Clinical validation of MSDTs must be rigorously pursued through prospective trials. Interoperability between data systems, especially integrating real-world data from heterogeneous sources, must be standardized. Ethical concerns, including data ownership, informed consent for model use, and ensuring equitable access to these technologies, require proactive governance frameworks (14). By simulating complex biological systems across scales, MSDTs promise not only to predict individual health trajectories but also to reshape them through optimized, dynamic, and personalized interventions. Their implementation marks a critical step towards realizing the vision of truly individualized, preventive, and precision healthcare.

The development and deployment of MSDTs in healthcare introduce profound ethical, regulatory, and infrastructural challenges that must be proactively addressed to ensure responsible innovation and equitable access.

A primary ethical concern is data privacy and ownership. MSDTs rely on extensive, longitudinal, and multi-source data, including sensitive genomic, behavioral, and environmental information. The aggregation and analysis of such data raise significant risks of re-identification, even when anonymized datasets are used (30). Traditional privacy protection measures, such as de-identification, may prove insufficient. Therefore, new governance models are required, emphasizing dynamic consent, secure federated learning architectures, and patient-controlled data access mechanisms (13).

Closely related is the issue of algorithmic fairness and bias propagation. Health disparities embedded in historical datasets, due to systemic inequities, can be amplified if not explicitly mitigated during the development of MSDTs. For example, underrepresented populations may experience less accurate predictions or suboptimal simulated interventions (31). Fairness audits, subgroup performance evaluations, and bias correction algorithms must become standard practice to ensure equitable clinical utility across demographic groups.

Transparency and explainability represent further critical dimensions. Clinicians and patients must be able to understand the rationale behind MSDT-generated recommendations, especially in high-stakes contexts like oncology or critical care. Techniques such as counterfactual explanations, SHAP (SHapley Additive exPlanations) values, and interpretable surrogate models should be systematically embedded within MSDT architectures (32). Transparency is not merely a technical feature; it is foundational to trust, informed consent, and effective shared decision-making.

On the regulatory front, current frameworks are ill-adapted to the complexities of dynamic, self-adaptive digital twins. Unlike static medical devices or predefined clinical decision support systems, MSDTs continuously evolve based on new data streams and reinforcement learning feedback loops. Regulatory bodies such as the FDA and EMA are beginning to explore adaptive approval pathways, but clear guidance on validation, risk categorization, and post-deployment monitoring for MSDTs remains urgently needed.

Validation itself poses major challenges. Conventional randomized controlled trials may be insufficient to assess continuously updating MSDTs. Alternative approaches, such as digital twin randomized simulations, real-world evidence collection, and adaptive trial designs, must be developed and accepted by regulatory authorities to ensure rigorous yet flexible evaluation (33).

Infrastructurally, interoperability is a bottleneck. MSDTs require seamless data integration across electronic health records, wearable sensors, genomic databases, and environmental monitoring systems. Yet, data silos, incompatible standards, and proprietary formats persist. The adoption of universal interoperability frameworks such as HL7 FHIR (Fast Healthcare Interoperability Resources) and OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) is essential to allow scalable and reproducible MSDT development (34).

Moreover, the computational demands of MSDTs, particularly for real-time simulation, reinforcement learning, and high-dimensional data fusion, necessitate robust, secure, and scalable infrastructures. Cloud-based architectures with strong encryption, federated learning platforms, and edge computing solutions will be critical to meet latency and privacy requirements (35).

Then, social and cultural factors must not be overlooked. Patient acceptance of MSDTs will depend not only on technical performance but also on perceived benefits, risks, and alignment with individual values. Co-design approaches involving patients, clinicians, ethicists, and technologists are crucial to ensure that MSDTs serve humanistic goals rather than merely technological advancement (36).

While multi-scale digital twins hold transformative potential, their responsible deployment demands a comprehensive ethical, regulatory, and infrastructural ecosystem. Proactively addressing these challenges will be pivotal in ensuring that MSDTs fulfill their promise of enhancing health outcomes, reducing disparities, and fostering trust in an increasingly data-driven era of medicine.

While interdisciplinary collaboration is fundamental to the development of multi-scale digital twins, global data diversity is equally critical to ensure model robustness, fairness, and clinical generalizability across populations, healthcare systems, and socio-environmental contexts (37). MSDTs trained exclusively on geographically or ethnically homogeneous datasets risk limited external validity and the amplification of structural biases (38).

However, international collaboration in this domain is constrained by heterogeneous data sovereignty and privacy regulations, including the General Data Protection Regulation (GDPR) in the European Union, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the Personal Data Protection Act (PDPA) in Singapore, and the Chinese Cybersecurity Law and Data Security Law (39–41). These frameworks impose differing requirements regarding data localization, cross-border transfer, consent, and secondary use, complicating the construction of globally integrated digital twin infrastructures.

Several technical and governance mechanisms can enable meaningful international collaboration without violating sovereignty constraints. Federated learning architectures allow MSDT models to be trained across distributed datasets while keeping sensitive patient-level data within national or institutional boundaries (13, 42). Only encrypted model parameters or gradients are exchanged, substantially reducing legal and ethical risks associated with cross-border data transfer. Complementary privacy-preserving techniques, including secure multi-party computation, homomorphic encryption, and differential privacy, further mitigate re-identification risks in international model development (13, 43, 44).

At the organizational level, emerging concepts such as data trusts, data cooperatives, and international research data spaces provide legal and contractual frameworks to govern access rights, usage limitations, liability, and benefit sharing across jurisdictions (45). In parallel, regulatory sandboxes operated by health authorities offer controlled environments for testing cross-border digital health and AI systems under real-world conditions while maintaining regulatory oversight (46).

From a strategic perspective, harmonization efforts should focus not on uniform legislation, which is politically unrealistic, but on interoperability of compliance. This includes standardized consent models, metadata schemas describing regulatory constraints, machine-readable data-use policies, and international certification mechanisms for privacy-preserving digital twin platforms (47–49).

Ultimately, scalable and ethically responsible MSDTs will require a dual foundation: local compliance with national data sovereignty laws and global coordination through technical standards and governance frameworks. Without such international alignment, digital twins risk reinforcing existing data monopolies and regional biases rather than enabling truly universal personalized medicine.

To fully realize the transformative potential of MSDTs in healthcare, a coordinated and strategic roadmap is essential. This roadmap must span data infrastructure, methodological innovation, regulatory alignment, and interdisciplinary collaboration.

There is a critical need to build multi-scale biobanks and longitudinal cohorts. Traditional clinical cohorts often lack the temporal depth, biological breadth, and environmental diversity required for MSDTs. New initiatives must integrate genomic, proteomic, imaging, behavioral, and environmental data collected over time, ideally beginning early in life and continuing across the lifespan (50). Initiatives like the UK Biobank and the All of Us Research Program provide templates but must be extended to encompass dynamic, multi-modal, and ethnically diverse populations.

Promoting interoperability and data standards is crucial. Standardization initiatives such as HL7 FHIR for clinical data and GA4GH (Global Alliance for Genomics and Health) frameworks for omics data should be mandated or incentivized to ensure seamless integration into MSDT platforms (51). Interoperability is not merely technical; it is foundational for collaboration, reproducibility, and scalability.

Methodological innovation must prioritize hybrid modeling approaches. Combining mechanistic models grounded in biology with machine learning models enables both interpretability and predictive flexibility (12). Emphasis should be placed on developing hybrid AI systems that can simulate causal relationships, handle uncertainty, and adapt over time as new data become available.

Cross-disciplinary consortia should be established to bridge gaps between clinicians, computer scientists, systems biologists, ethicists, and policymakers. Initiatives such as the European Virtual Human Twin project exemplify the power of collaborative efforts. Institutional support and funding mechanisms must favor interdisciplinary, multi-institutional partnerships over soiled research (52).

Regulatory frameworks must adapt to the dynamic nature of MSDTs. Regulators should develop new categories for continuously learning systems, establishing guidelines for safe updates, ongoing validation, and transparent reporting of model evolution. Regulatory sandboxes, controlled environments where innovative technologies can be tested under regulatory supervision, should be expanded for MSDTs.

Sixth, ethics and governance must be embedded from the outset. MSDT development should follow principles of responsible innovation, including anticipatory ethics, participatory design involving patients, and proactive bias mitigation strategies (14). Governance frameworks should ensure not only data protection but also the equitable distribution of benefits derived from digital twin technologies.

Investment in education and capacity building is vital. Clinicians must be trained to interpret, validate, and integrate MSDT outputs into clinical decision-making. Similarly, engineers and data scientists must gain deeper understanding of medical contexts and ethical responsibilities. Dedicated curricula at the intersection of AI, systems medicine, and bioethics should be developed to cultivate the next generation of MSDT researchers and practitioners.

Very few MSDT implementations have undergone large-scale clinical validation or prospective evaluation in real-world settings. Consequently, some proposed applications discussed herein are extrapolations based on technological potential rather than proven clinical efficacy. The integration of multi-scale data (from omics to clinical and behavioral layers) poses significant technical, infrastructural, and interoperability challenges. Many reviewed studies focus on single-scale or bi-scale models rather than fully integrated frameworks. Therefore, the generalizability of current methodologies to true multi-scale clinical practice remains uncertain. Because the structured narrative synthesis prioritized papers explicitly discussing multi-scale digital twins, some clinically deployed single-domain digital twins that do not self-identify as “multiscale” may be underrepresented in the clinical examples. This work therefore interprets current clinically mature twins as “proto-MSDTs” and discusses the technical requirements needed to extend them toward fully integrated multi-scale architectures.