Written by Technical Team | Last updated 24.10.2025 | 17 minute read
Human biology is staggeringly complex. Every heartbeat, every inflammatory response, every fluctuation in hormone levels reflects an interplay of genetics, environment, behaviour and time. For decades, healthcare innovation has tried to understand this complexity through clinical trials, data analysis and physical models. But these approaches are slow, expensive and ethically constrained, and they tend to produce insights at the population level rather than for the individual standing in front of a clinician. Digital twin technology aims to change that completely. By creating a computational replica of a human organ, system or even an entire person – continuously updated with real-world data – digital twins allow us to simulate how a body might respond before we intervene in the real world. In digital health development, this is more than an interesting tool. It has the potential to redefine how we design medical devices, test drugs, deliver personalised treatment, and monitor patients continuously and safely.
This article explores how digital twins work in the context of human physiology, where they are already being used, and what stands between today’s prototypes and large-scale clinical deployment. It also looks at why digital twins could become one of the most important levers for reducing healthcare costs while improving outcomes, especially in areas like cardiology, oncology and chronic disease management.
In engineering, a digital twin is a dynamic, virtual representation of a physical asset – a jet engine, a wind farm, a factory floor – that is fed by real-time sensor data so that it behaves like its real counterpart. In healthcare, the physical asset is the human body, or a subcomponent of it such as a heart, lung, tumour or metabolic system. This means that a digital twin of a patient is not just a static 3D model. It is a computational entity that can ingest longitudinal patient data, simulate physiological processes, and predict likely outcomes under different scenarios. That level of predictive power is precisely what clinical decision-making struggles to achieve in practice.
There are different “levels” of digital twins in health. At one end of the spectrum you have organ-level twins built for a specific purpose, for example, a cardiac twin that models blood flow dynamics, valve performance and electrical conduction in a particular patient’s heart. At the other end you have system-level twins that integrate multiple biological processes, such as immune function, metabolism, circulation and drug kinetics, in an attempt to represent the whole patient. The more data you can feed the twin, the more it begins to capture not just anatomy but response: how this specific body behaves over time, under stress, on medication, at rest and during recovery.
The reason this matters is that living systems are not linear. Two people with identical diagnoses and the same prescription may experience completely different trajectories. One might stabilise; the other might deteriorate dangerously. A clinician can’t ethically run hundreds of “what if?” experiments on a living person to test every variation in dose, timing or therapy combination. A digital twin can. It can be used to forecast risk before it materialises, optimise a treatment plan and, importantly, justify that plan with explainable physiological reasoning rather than opaque probability.
For health systems strained by ageing populations and chronic disease, that is powerful. If you can intervene earlier, in a more targeted way, you prevent hospital admissions, reduce complications and improve quality of life. And because the twin is software, those insights can be delivered at scale, remotely, through digital health platforms rather than always in person. This is where the link to digital health development becomes obvious: digital twins are not just clinical research toys. They are becoming engines for new classes of medical software, decision support tools, companion apps for implants and even regulatory submissions for high-risk devices.
Building a medically useful digital twin of a human system requires several layers of input and intelligence. In simple terms, it blends three worlds that historically sat apart: biomedical imaging, physiological modelling and machine learning. Each contributes something essential.
First, there is structural data. This includes imaging data such as MRI, CT, ultrasound and high-resolution 3D scans. From this, you can reconstruct a patient’s specific anatomy: the exact geometry of their left ventricle, the stiffness of a stented artery, the volume of a tumour relative to surrounding tissue. Structural accuracy is critical because small variations in shape and thickness can radically alter mechanical function and blood flow.
Second, there is functional and temporal data. Wearables, implanted sensors, blood biomarkers, lab results, electronic health records and patient-reported outcomes all contribute here. This data describes how an individual’s body behaves over time. Are they tachycardic at night? How fast does their blood pressure recover after exertion? How does glucose respond to a given meal or a missed dose of medication? This time-series information allows the twin to evolve dynamically rather than freeze as a one-off snapshot. A digital twin that does not update risks becoming clinically irrelevant within weeks.
Third, there is the physiological model. This is where domain knowledge of biology and physics is encoded. For example, a cardiovascular twin will include models of haemodynamics (how blood moves), myocardial contractility (how heart muscle fibres shorten and relax), electrical conduction pathways and valve dynamics. For a respiratory twin, it might be airflow resistance, gas exchange efficiency and lung compliance. These models can draw on decades of mechanistic research, meaning they are grounded in causal relationships: if resistance in this artery increases, pressure here will rise, which will in turn change flow there. That causal scaffold is what makes the twin explainable.
Finally, there is a computational engine that fuses observed data from the patient with the physiological model to calibrate it. This is where machine learning often plays a role. The twin must constantly adjust its parameters so that its simulated outputs (for example, predicted heart strain during exertion) match the real patient’s measured outputs (for example, strain observed in an echocardiogram during a stress test). The more often and more accurately you can calibrate, the closer the twin gets to “truth”, and the more confidently you can use it to test alternative futures.
At a practical level, many digital twin platforms in development are built using the following kinds of data sources, each of which can be linked to secure digital health infrastructure:
What sets digital twins apart from traditional predictive models is that they are designed to operate at the level of “me”, not “people like me”. They aim to answer, “What will happen to this specific person if we do X right now?” rather than “What usually happens to a cohort with similar traits?” That shift towards high-resolution personal simulation changes how digital tools are conceived, validated and deployed.
Some of the most compelling early uses of digital twin technology are happening in cardiology, oncology, orthopaedics and chronic condition management. These are areas where:
Cardiology is often seen as the flagship use case. A personalised cardiac twin can be used to virtually test an implantable device, such as a valve prosthesis or stent, before it ever enters the patient’s body. A cardiologist can simulate how different device sizes or placements would affect blood flow, turbulence and wall stress in that particular heart. This can reduce the risk of complications and cut down the number of invasive exploratory procedures. It is essentially pre-operative rehearsal, but with physics-informed feedback rather than guesswork. It also supports regulatory approval of new cardiovascular devices, because it allows developers to generate large numbers of physiologically realistic scenarios without exposing real patients to early prototypes.
In cancer care, digital twins are being developed to model tumour growth and treatment response. By integrating imaging, biopsy data, genomic information and pharmacokinetic models of specific drugs, an oncological twin can forecast how a given tumour is likely to react to different treatment regimens. Rather than relying purely on guidelines, clinicians could see, in silico, whether an aggressive chemotherapy schedule is likely to shrink a tumour in a particular patient or whether it is more likely to cause toxicity without meaningful benefit. In parallel, pharmaceutical companies can use these models to run virtual trials across thousands of synthetic patient twins to explore optimal dosing, sequence of combination therapies and resistance patterns, accelerating drug development.
Orthopaedics offers another clear illustration. Before fitting a joint replacement, surgeons and device engineers can build a musculoskeletal twin of the patient’s joint mechanics, bone density distribution and gait pattern. They can then test how different implant geometries will perform under real walking loads, not generic averages. This means implants can be sized and oriented for maximum stability, minimum wear and best functional outcome. It also supports post-surgical rehabilitation planning, because the twin can simulate how load distribution will change as muscle strength returns.
Digital twins also have enormous value in chronic and lifestyle-driven disease areas such as diabetes, hypertension and respiratory illness. Here, the twin can act less like a surgical rehearsal tool and more like a living health companion. For instance, a metabolic twin could integrate glucose levels from a continuous glucose monitor, insulin dosing records, meal timing and macronutrient breakdown, activity levels and sleep quality to build a personalised model of a person’s glycaemic control. This model could then predict which meals are likely to cause dangerous spikes, how late-night stress affects insulin sensitivity the next morning, or how a minor adjustment in dose timing might smooth out variability across the day. That becomes a powerful feedback engine inside digital therapeutics and remote patient monitoring platforms.
Perhaps the most transformative vision, though, is in intensive care and high-acuity environments where speed and accuracy save lives. In critical care, clinicians continuously make trade-offs when titrating ventilation, fluids and medication. A patient-specific digital twin that can rapidly model the downstream effects of those trade-offs could reduce the trial-and-error aspect of acute care and help stabilise patients faster, with fewer complications. The long-term implication is that virtual physiology could become a vital sign in its own right – something clinicians consult alongside heart rate and oxygen saturation.
Digital health is no longer just apps and dashboards. Increasingly, it includes software-defined medical devices, connected implants and AI-powered decision support systems. All of these must navigate strict clinical validation and regulatory scrutiny. This is one of the least glamorous but most commercially important reasons digital twins are attracting attention: they can compress the product development lifecycle for new medical technologies while improving safety.
Bringing a new high-risk medical device to market typically requires in vitro testing, in vivo testing (often in animal models), and staged human trials. Each phase is slow, expensive and dependent on recruitment, ethics approval and clinical capacity. A physiological digital twin can play two roles here.
First, it can serve as a test bed for iterative design. Engineers can modify parameters of a prototype – geometry, material stiffness, actuation profile, energy delivery – and instantly see how those changes would affect tissue interaction, fluid dynamics or thermal load in relevant anatomy. Because the environment is simulated, you can explore edge cases and rare anatomies that would be difficult or impossible to source in early trials. That means you can “fail fast” without real harm.
Second, and increasingly important, regulators are beginning to acknowledge the value of in silico trials when supported by robust physiological models and evidence that those models reflect reality. Virtual cohorts of patient twins can be exposed to a new device, and their simulated responses can generate distributions of outcomes, risk profiles and performance limits. These virtual trial results do not replace human trials, but they can reduce the size and duration of later-phase trials by narrowing uncertainty and helping sponsors propose smarter, more targeted study designs.
For digital health companies, this is commercially attractive because it can dramatically lower cost of evidence. Intelligent remote monitoring platforms, for instance, can be paired with a patient twin to demonstrate that the system not only measures but also meaningfully interprets physiological data in a way that supports clinical decision-making. Instead of waiting for years of outcomes data, companies can present regulator-ready evidence that “If the system had been deployed in this virtual cohort of 10,000 patients, here is how many exacerbations we would have predicted and prevented, here is how treatment would have been escalated, and here is how many admissions might have been avoided.” That type of modelled health-economic argument is becoming central to reimbursement and commissioning discussions in many health systems.
Digital twins are also useful post-market. Once a device has been approved and implanted, a digital twin of the recipient’s anatomy can be used to monitor long-term fit and function, predict failure modes, or personalise maintenance schedules. In other words, the twin doesn’t just get you to market; it helps keep the product effective and safe throughout its lifecycle, and it feeds real-world performance data back into next-generation design.
It is tempting to view digital twins as inevitable. The logic is compelling, the early case studies are persuasive, and the broader health system pressures – staffing shortages, rising chronic disease, ageing populations – are not going away. But there are serious barriers that must be addressed before digital twins can be embedded safely and equitably into routine care at scale.
The first barrier is data quality and interoperability. A digital twin is only as good as its inputs. Many health systems still struggle with fragmented records, inconsistent coding, missing values and device data that lives in proprietary silos. If the data feeding the twin is incomplete, biased or stale, the predictions will be misleading. Worse, misleading clinical guidance can cause harm. Solving this means building robust data pipelines, common data formats, and governance frameworks that allow relevant information to flow securely from scanners, wearables, electronic health records and patient apps into the simulation layer in near real time. It also means addressing basic identity resolution: the twin must remain accurately mapped to the right person across every update.
The second barrier is validation and trust. Clinicians are rightly sceptical of “black box” tools. For a digital twin to support real-world decision-making, it must be transparent enough that a clinician can interrogate its reasoning. If the twin recommends a change in drug dosage, for example, the clinician needs to see why. Was it driven by declining renal function? Altered circulation? Slower clearance of active compound overnight? Trust will grow when the model is not just accurate but interpretable and auditable. This is why twins that combine mechanistic, physiology-based models with machine learning are often viewed as more clinically acceptable than purely statistical models trained on historical data alone.
The third barrier is regulatory alignment. In most jurisdictions, any software that meaningfully influences diagnosis or treatment is regulated as a medical device. That includes software that generates predictions, triage recommendations or scenario analysis. Digital twin platforms that simulate individual physiology in order to steer clinical action are therefore squarely in scope. Developers must be ready to meet stringent safety, performance, cybersecurity and post-market surveillance requirements. They must also prove that the twin’s recommendations are appropriate across patient subgroups and do not encode structural bias. This is particularly important for cardiovascular and metabolic twins, where underlying datasets have historically under-represented women, ethnic minorities and certain age groups.
The fourth barrier is ethical use. A human digital twin contains intensely sensitive information. It is more than a medical record; it is a predictive map of how your body might behave under different stressors. Questions arise quickly. Who owns that model – the patient, the hospital, the software vendor? Can it be used to make insurance decisions? Can it be shared with a pharmaceutical company to design targeted therapies, and if so, under what consent terms? Could it be subpoenaed in a legal case? Health systems will need clear ethical frameworks that treat digital twins as extensions of the patient, not just datasets or product features. Consent will need to evolve from “we collect your data” to “we generate a simulation of you and we may test treatments on it virtually”. That is conceptually different and must be communicated honestly.
Finally, there is the human factor: how digital twins change the clinician–patient conversation. In theory, twins enhance shared decision-making. Imagine sitting with a cardiologist and watching a simulation of your own heart respond to different valve replacement strategies, with colour-coded maps of stress and flow. That is an extraordinary way to explain risk, benefit and trade-offs. It personalises consent and could reduce anxiety because patients can “see” what is proposed. But there is a flip side. If the twin forecasts a poor prognosis under all plausible therapies, delivering that information becomes a delicate ethical act. Simulation might move hard conversations earlier in the care journey, and clinicians must be prepared to handle that with empathy.
Even with these barriers, the momentum behind digital twins in health is undeniable. Several trends are driving this forward at pace:
Looking ahead, the most interesting evolution may be the move from reactive twins to proactive twins. Today, most deployments focus on supporting a specific upcoming decision – a surgery, a dosing choice, a rehabilitation plan. In the near future, twins may run quietly in the background of a patient’s digital health ecosystem, constantly scanning for deviations from expected patterns and prompting subtle adjustments before issues escalate. Your cardiac twin might detect that your recovery curve after exertion is slowly flattening week on week, signalling early cardiac fatigue before you feel symptomatic breathlessness. Your respiratory twin might alert your clinician that, based on your airflow patterns, you are heading towards an exacerbation of asthma within the next 48 hours unless your inhaler routine changes. At that point, the line between monitoring and intervention blurs, and “digital health” becomes truly physiological, not just behavioural.
The idea of a precise virtual copy of a human being naturally invites philosophical questions. Are we comfortable with software that can, in a sense, rehearse our illness? Will we accept treatment plans that have not yet been tried in any biological human, only in a simulated one that happens to be our own? These are not science fiction questions any more. They are fast becoming practical questions for clinicians, regulators, developers and patients, because digital twins are moving from research labs into clinical workflows and commercial digital health products.
One thing is clear: simulating human physiology changes the stakes. It introduces a layer of foresight into medicine that has historically been missing. With foresight comes responsibility. Health systems that learn to use digital twins safely, transparently and equitably will gain not only efficiency, but also a fundamentally different relationship with risk – one that shifts from reacting to problems after they occur, to shaping the future path of the patient before the problem truly begins.
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