AI-powered digital twins could become backbone of future healthcare

The study shows how AI-based digital twins are transforming healthcare institutions themselves. Hospitals are increasingly complex systems where patient flow, staffing, equipment availability, and emergency response must be carefully coordinated. Traditional management approaches struggle to account for this complexity, particularly during periods of high demand or crisis.

Artificial intelligence is moving healthcare toward a new operational model in which clinical decisions, hospital management, and even public health planning can be tested in virtual environments before being applied in the real world. This shift is being driven by the rapid development of AI-based digital twins, virtual replicas of patients, processes, and health systems that continuously evolve using real-time data. 

The scope and implications of this transition are examined in the study From Local to Global Perspective in AI-Based Digital Twins in Healthcare, published in Applied Sciences. The review analyzes how AI-powered digital twins are being deployed across multiple levels of healthcare, from individualized patient modeling to hospital operations and population-scale health management, while also outlining the ethical, technical, and regulatory challenges that must be resolved before widespread adoption.

Patient-level digital twins reshape personalized medicine

Digital twins continuously update as new data arrive from electronic health records, medical imaging, laboratory tests, and wearable or implantable sensors.

By integrating AI algorithms with these data streams, patient-level digital twins can simulate disease progression, forecast treatment responses, and support precision medicine. Clinicians can evaluate multiple therapeutic strategies virtually, reducing uncertainty before real-world interventions. This approach is especially valuable for complex or chronic conditions, where disease trajectories vary widely between individuals.

The review highlights applications in neurology and mental health, where digital twins are used to model brain function, cognitive decline, and behavioral patterns. In psychiatric care, AI-driven twins can help identify early signs of deterioration or relapse by detecting subtle changes in patient data over time. In physiotherapy and rehabilitation, digital twins are used to simulate musculoskeletal dynamics, allowing therapy plans to be adjusted based on predicted recovery outcomes.

Chronic disease management represents another major use case. Digital twins can track metabolic, cardiovascular, or respiratory parameters, enabling continuous risk assessment and proactive intervention. Instead of relying on periodic clinical visits, healthcare providers can monitor patient trajectories in near real time, improving early detection of complications.

The study also addresses the growing role of generative AI in patient-level digital twins. Generative models are increasingly used to fill gaps in incomplete datasets, generate synthetic patient data for research, and support training and simulation without exposing sensitive personal information. This capability is particularly important in rare diseases, where real-world data are often limited.

However, the authors caution that patient-level digital twins raise significant challenges around data accuracy, model validation, and ethical oversight. A digital twin is only as reliable as the data and assumptions underlying it. Biases in training data or incomplete clinical records can propagate errors, potentially affecting clinical decisions. Ensuring transparency, accountability, and clinician oversight remains essential.

Hospital and system digital twins transform healthcare operations

The study shows how AI-based digital twins are transforming healthcare institutions themselves. Hospitals are increasingly complex systems where patient flow, staffing, equipment availability, and emergency response must be carefully coordinated. Traditional management approaches struggle to account for this complexity, particularly during periods of high demand or crisis.

Hospital-level digital twins address this challenge by creating virtual replicas of clinical workflows and infrastructure. These models allow administrators to simulate operational changes, test resource allocation strategies, and predict bottlenecks before they occur. By using AI to analyze historical and real-time data, digital twins can forecast patient admissions, optimize bed utilization, and improve scheduling of staff and procedures.

The review notes that digital twins proved especially valuable during public health emergencies, when hospitals faced sudden surges in demand and rapidly changing conditions. Virtual simulations helped decision-makers assess the impact of policy changes, such as reallocating intensive care resources or altering patient triage protocols, without disrupting real-world operations.

AI-based digital twins also support quality and safety improvements. By modeling care pathways, hospitals can identify inefficiencies or risk points that contribute to medical errors or delays. Predictive analytics embedded within digital twins can alert staff to potential system failures, equipment shortages, or staffing gaps before they compromise patient care.

Interoperability emerges as a key issue at the institutional level. Hospitals rely on multiple information systems, often developed by different vendors and governed by varying standards. Integrating these systems into a unified digital twin requires robust data governance frameworks and standardized interfaces. The study emphasizes that without interoperability, the full benefits of hospital-level digital twins cannot be realized.

Cybersecurity is another concern highlighted by the authors. As digital twins aggregate sensitive operational and clinical data, they become attractive targets for cyberattacks. Ensuring data integrity, access control, and system resilience is critical, particularly when digital twins are used to inform real-time decision-making.

From population health to global digital twin ecosystems

At the population scale, AI-based digital twins can model disease trends, healthcare demand, and the impact of policy interventions across communities. By integrating epidemiological data, demographic information, and environmental factors, these models support more informed public health planning.

Population-level digital twins enable health authorities to test scenarios such as vaccination strategies, screening programs, or resource distribution plans before implementing them. This capability is especially valuable in managing chronic disease burdens and preparing for future health crises. By simulating different intervention strategies, policymakers can evaluate trade-offs between cost, effectiveness, and equity.

At the national level, interconnected digital twins can support healthcare system reform and long-term planning. Governments can assess how changes in funding, infrastructure investment, or workforce policy may affect access to care and health outcomes over time. These models also help identify systemic vulnerabilities that could undermine resilience during emergencies.

The authors take the concept further by discussing the potential for global digital twin ecosystems. Interoperable digital twins across countries could support international research collaboration, disease surveillance, and coordinated responses to global health threats. In this vision, AI-driven digital twins become shared analytical tools that help align health strategies across borders.

However, the review makes clear that this global ambition faces substantial barriers. Data sovereignty, regulatory differences, and ethical standards vary widely between jurisdictions. Sharing health data across borders raises complex legal and governance questions, particularly around privacy and consent. Without harmonized frameworks, global digital twin systems risk fragmentation or misuse.

Ethical governance is a recurring theme throughout the study. The authors stress that digital twins should augment, not replace, human judgment. Decisions informed by AI models must remain accountable to clinicians, administrators, and policymakers. Clear standards are needed to define responsibility when digital twin recommendations influence outcomes.

Validation and trust are also key challenges. For digital twins to be adopted widely, stakeholders must trust that models accurately reflect reality and respond appropriately to change. Continuous validation against real-world outcomes, transparent reporting of model limitations, and inclusive stakeholder engagement are identified as essential steps.