Advanced Simulation in Healthcare with Digital Twins, MATLAB & Simulink
Digitalization in healthcare has traditionally concentrated on collecting and analysing information: electronic medical records, diagnostic images, laboratory results and data from medical equipment. Digital Twins take this concept a significant step further. A Digital Twin creates a dynamic virtual representation of a physical system that can be used to understand its current behaviour, simulate possible changes and investigate what may happen under different conditions. When combined with mathematical modelling, real-world data and advanced simulation, Digital Twins have considerable potential in healthcare. MATLAB and Simulink provide many of the computational and modelling capabilities required to develop such systems, allowing researchers and engineers to construct models, incorporate patient or equipment data, run simulations and evaluate alternative scenarios without initially having to test every possibility on a patient or physical medical system.
What is a Digital Twin?
A Digital Twin is a virtual representation of a physical object, process or system that is connected, directly or indirectly, to information about its real-world counterpart. Unlike a conventional computer model, which may represent a system under predefined assumptions, a Digital Twin can evolve as new data becomes available. In healthcare, the physical counterpart may be a medical device, an organ, or a physiological process.
Consider a simplified virtual model of a human heart. A generic simulation might describe how a typical heart behaves according to established physiological principles. A Digital Twin could go further by incorporating information relating to a particular patient, such as heart rate, ECG measurements, medical images or other relevant physiological parameters. As further information becomes available, the virtual model can potentially be updated to provide a more individualised representation.
Digital Twins should therefore not be confused with simple three-dimensional anatomical models. Their principal value lies not merely in reproducing appearance, but in representing behaviour and enabling simulation.
Why Simulation Matters in Healthcare
Healthcare presents an unusually compelling case for simulation because experimentation involving patients is necessarily constrained by considerations of safety and ethics. A clinician cannot simply try numerous alternative interventions on the same patient to determine which produces the most favourable result. A Digital Twin adds another dimension by allowing the simulation to incorporate information associated with the actual system being represented. The objective is to move from a broadly representative model towards one that more closely reflects a particular device, process or patient.
This is where MATLAB and Simulink can play an important role.
Building Healthcare Digital Twins with MATLAB
MATLAB provides an environment for numerical computation, data analysis, algorithm development and visualisation. These capabilities are fundamental to Digital Twin development because a useful healthcare model may need to combine information from several sources. Patient-related data, for example, could include biosignals such as ECG, EEG or respiratory measurements, laboratory observations, medical images and information obtained from wearable sensors. Before these data can contribute to a model, they may require cleaning, filtering, synchronisation, transformation and analysis. MATLAB can be used to perform these operations and to identify parameters required by the Digital Twin.
While MATLAB provides extensive computational and data-analysis capabilities, Simulink is particularly valuable when the system being represented is dynamic. Its graphical modelling environment allows engineers and researchers to represent systems as interconnected functional components and simulate their behaviour over time. This is highly relevant to healthcare because the human body is inherently dynamic.
Exploring Alternative Scenarios
One of the most significant capabilities of a Digital Twin is the ability to perform ‘what-if’ analysis. Suppose a model represents an aspect of cardiovascular behaviour. Rather than observing only the patient’s current condition, researchers could alter selected model parameters and simulate how the system responds. Multiple scenarios could be evaluated computationally and compared.
In another application, engineers developing a medical monitoring or therapeutic device could simulate variations in patient physiology, sensor behaviour or operating conditions. This allows them to explore circumstances that may be difficult, expensive or unsafe to reproduce physically.
MATLAB also enables simulations to be automated. Instead of testing individual parameter combinations manually, developers can conduct large numbers of simulation runs, systematically varying inputs and analysing the resulting outputs. Optimization techniques can then be used to search for parameter combinations that satisfy specified objectives or constraints. This ability to explore alternatives is central to the value of Digital Twins. The virtual model becomes not simply a representation of reality, but an environment in which possible future behaviour can be investigated.
Potential Relevance to Indian Healthcare
Digital Twins could be particularly valuable in India, where a large and diverse population must be served across healthcare environments ranging from major specialist hospitals to smaller facilities and remote-care systems. Patient-specific modelling combined with telemedicine and connected monitoring could eventually help specialists evaluate complex information without requiring every stage of assessment to occur at a tertiary hospital. Digital Twins could also assist Indian medical-device developers by allowing extensive virtual testing before physical prototypes and clinical evaluations. Equally important, developing models using appropriate Indian clinical and physiological datasets could help ensure that future Digital Twin applications reflect the characteristics of the populations they are intended to serve.
Towards More Predictive Healthcare
Digital Twins represent an important evolution from recording healthcare data towards using that data to construct dynamic, testable representations of real-world systems. Their promise lies in the ability to ask not only what is happening, but also what could happen if conditions change.
MATLAB and Simulink provide complementary capabilities for this purpose. MATLAB can process physiological and clinical data, develop analytical and AI algorithms and estimate model parameters, while Simulink can represent dynamic systems and simulate their behaviour under changing conditions. Together, they provide an environment in which healthcare Digital Twins can be developed, tested and progressively refined.
As models, computing capabilities and healthcare data improve, Digital Twins could become increasingly valuable for medical research, device development, personalised modelling and clinical decision support. Their ultimate contribution will depend not merely on creating sophisticated simulations, but on ensuring that those simulations are scientifically valid, clinically meaningful and capable of converting complex healthcare data into useful insight.
Summary
A Digital Twin is a dynamic virtual representation of a real-world object, process or system that can evolve as new data becomes available. In healthcare, Digital Twins can represent medical devices, physiological processes and potentially aspects of individual patients. MATLAB provides capabilities for processing clinical and biosignal data, extracting relevant parameters, developing algorithms and applying machine learning, while Simulink enables dynamic systems to be modelled and simulated over time. Together, they allow researchers and engineers to perform ‘what-if’ analyses, investigate patient-specific scenarios and virtually test medical technologies. With appropriate validation, data protection and clinical oversight, Digital Twins could support personalised healthcare, medical-device development, remote monitoring and more informed healthcare decision-making.
