Digital Twins in Healthcare

At Experion Technologies, our vision includes next-generation AI-driven human digital twins designed to anticipate health risks and enable proactive care.

Can you imagine a future where every patient has a living, breathing virtual replica? A world where a doctor can test a risky procedure on you- without you ever feeling a thing. What if they could predict how your body would respond to a new drug before you even took the first dose?

It sounds like a scene from a science fiction movie. But it is no longer fiction. This future is now possible through digital twins in healthcare technology, a technology rapidly transforming how hospitals diagnose, treat, and predict patient needs. One that can predict illnesses before symptoms appear, guide personalized treatments, and help hospitals prepare for emergencies before they happen.

Today, digital twin technology in healthcare is transforming clinical workflows and reshaping medical research. It is taking patient care to an entirely new level. As the global healthcare industry rapidly embraces advanced data-driven tools, digital twins in healthcare are emerging as one of the most groundbreaking and transformative innovations of the decade.

 

What Are Digital Twins in Healthcare?

A digital twin is a dynamic virtual model of a physical entity. In healthcare, a digital twin can refer to a patient, an organ, a medical device, or even an entire hospital workflow system. These digital replicas use real-time data to mirror the condition, performance, and behaviour of their real-world counterparts.

For example, some digital health twins include:

• A patient’s heart/ lung
• A physiology model
• A twin model of a hospital for capacity planning

How Digital Twin Technology Works?

The key difference between  digital twins in healthcare and a simple 3D model lies in its dynamism. While a 3D model offers a static snapshot, a digital twin creates a continuous, real-time feedback loop between the physical world and its virtual counterpart. So in essence,it doesn’t just represent an object or person -it evolves with them.

For the digital twin to accurately reflect real-world changes, it requires a continuous stream of data. This means capturing  and analysing information from multiple sources to show how different components interact. This “living” and updating aspect is what makes digital twin technology so revolutionary.

 

Data Sources That Power a Digital Twin

Digital twin technology in healthcare usually integrates:

• IoT sensors & wearables for real-time physiological signals such as heart rate, oxygen saturation, movement, sleep patterns, and more
• Electronic Health Records (EHR) and clinical data, including medical history, lab results, vitals, and treatment plans
• Medical imaging and diagnostic results such as MRI, CT, ultrasound, X-rays, and pathology reports
• Genomics and biomarker data to understand genetic variations, disease risk, and personalised treatment responses

Turning Data Into Intelligent Insights

By combining these diverse datasets, the digital twin can synchronize with real-world inputs and run simulations to predict various outcomes.This integration of data and simulation enables a level of predictability that traditional healthcare systems simply cannot achieve.

 

Why Digital Twins in Healthcare Matter: Market Growth & Adoption?

Industry Statistics

The momentum behind digital twins in healthcare is rapidly accelerating, driven by advances in AI, real-time data systems, and the push toward personalised, predictive medicine. Industry research reflects this exponential growth.

• According to a report by MarketsandMarkets, the global digital twins in healthcare market is valued at USD 4.47 billion in 2025 and is expected to grow at a remarkable CAGR of 68% between 2025 and 2030. This surge highlights the increasing demand for virtual patient models, operational simulations, and intelligent healthcare systems that can enhance decision-making and improve patient outcomes.

 

Use Cases of Digital Twins in Healthcare

1. Personalized Treatment & Precision Medicine

Patient-specific digital health twins allow clinicians to create highly accurate virtual models of an individual’s organs, biological functions, and disease patterns. These dynamic models simulate how a patient’s body might respond to different medications, treatment pathways, or lifestyle changes – much before applying them in real life.

Let’s see how this works in real clinical scenarios.

• Cancer Treatment: Digital twins can simulate tumour growth, therapy resistance, and the impact of chemotherapy or immunotherapy. This helps oncologists select the most effective treatment plan with fewer side effects.
• Diabetes Management: Virtual pancreas allows doctors to test insulin levels, glucose responses, and dietary changes, leading to more personalized diabetes control.
• Cardiac Care: Heart digital twins can model electrical activity, blood flow, and potential arrhythmias. Cardiologists use these simulations to select optimal interventions and reduce the risk of complications.

2. Virtual Organs & Digital Physiology Models

Most medical procedures are invasive, and their outcomes are known only after they are performed. This leads to significant risks to the patient. Here is where virtual organs come in. They are nothing but high-fidelity models that simulate how real human organs behave under different conditions. By integrating imaging data, biosignals, and patient history, these virtual organs can provide insights that are often impossible to obtain through traditional methods.

Some real-world applications include

• Virtual Heart Twins
  Advanced cardiac twins simulate electrical activity, blood flow, and tissue movement within the heart.Cardiologists can, in turn, use these models to:
  a. Predict arrhythmia risks and irregular heartbeats.
  b. Evaluate potential outcomes of catheter ablation
  c. Personalise treatment plans for heart failure or valve disorders
  d. Test medical devices such as pacemakers and stents in a virtual environment.These simulations significantly improve clinical accuracy and reduce procedural complications. One of the most widely recognised examples is the Dassault Systèmes living heart project, which developed a fully functional, anatomically accurate digital twin of the human heart.
• Liver Twins for Drug Toxicity Testing
  Digital liver twins replicate how the human liver processes, detoxifies, and metabolises substances, offering researchers a highly accurate, non-invasive tool for analysing drug behaviour. By integrating biochemical pathways, enzyme activity, and patient-specific data, these models enable safer and more efficient drug development.They are widely used to:
  a. Predict toxicity levels during early-stage drug trials
  Before a compound is ever tested on humans, liver twins can simulate metabolic breakdown, identify toxic by-products, and flag potential organ stress or failure scenarios.
  b. Reduce reliance on animal testing
  Because liver twins more closely mimic human physiology, they help reduce the need for animal studies.
  c. Assess how different patient groups may react to new medications
  Researchers can model scenarios across demographics and conditions.All of this accelerates drug development, lowers research costs, and ensures greater patient safety long before clinical trials begin.

3. Surgical Planning and Training

Digital twin technology is transforming how surgeons prepare for complex procedures. By creating a patient-specific virtual model, clinicians can rehearse surgeries in a simulated environment that mirrors real-life anatomy, physiology, and potential complications.

How Digital Twins Enhance Surgical Planning

a. Procedure rehearsal:Surgeons can practise step-by-step interventions on a patient’s digital model. So when it comes to the actual surgery, they would be familiar.
b. Risk prediction: The twin can simulate potential complications such as excessive bleeding, tissue damage, or arrhythmias, helping teams prepare contingency plans.
c. Tool and implant customisation: Digital models allow surgical teams to select or design implants and devices that perfectly fit the patient’s organ anatomy.

Training and Skill Development

Thus, from the above examples, it is clear that Digital twins play a pivotal role in medical education and skill enhancement. Trainees can practise on virtual models without risk. Realistic simulations improve hand-eye coordination, and, as they say, practice makes perfect. Repeated practice on diverse scenarios helps build confidence.

4. Hospital Workflow Optimization

A digital twin for healthcare systems enables hospitals to model and optimise their internal operations with accuracy. Creating a virtual replica of clinical workflows, resource utilisation, and patient movement within the hospital is a game-changer. This way, healthcare administrators can identify inefficiencies and predict demand, thereby enhancing capacity planning.

Within the hospital setting, digital twins in healthcare can simulate factors such as:

• Bed Occupancy-Forecasts admission rates, discharge patterns, and bed availability to prevent overcrowding.
• ICU Capacity- Models changes in critical care demand, helping hospitals plan for surges, emergencies, or seasonal fluctuations.
• Staff Allocation- Optimises shift scheduling, identifies staffing shortages, and improves team distribution across departments.
• Equipment Utilisation- Tracks real-time usage of ventilators, imaging devices, operating theatres, and other critical equipment to ensure efficient deployment.Digital twin for healthcare systems is useful – especially in high-demand periods, as it can help organisations deliver smoother and more reliable patient care. Emergency preparedness will no longer be last-minute. By eliminating resource bottlenecks, they can always stay one step ahead!

5. Predictive Maintenance of Medical Devices

Digital twin technology is also transforming hospital engineering and biomedical maintenance. By creating virtual replicas of medical devices and equipment, healthcare organisations can monitor performance in real time and detect issues before they cause failures. These predictive capabilities help hospitals avoid unexpected downtime, reduce repair costs, and ensure uninterrupted patient care,especially in departments that rely heavily on advanced devices.

Digital twins in healthcare continuously collect and analyse data from equipment such as Temperature and pressure sensors,Usage patterns, Mechanical stress indicators, and electrical anomalies. AI models can interpret this data to identify patterns that signal early signs of malfunction or wear and tear.

Benefits for Healthcare Providers

• Early Fault Detection: Potential failures are flagged well in advance, allowing maintenance teams to intervene proactively.
• Increased Equipment Uptime: Critical devices- such as MRI machines, ventilators, infusion pumps, and dialysis units-remain available when patients need them most.
• Cost Efficiency: Predictive maintenance reduces emergency repairs, extends equipment lifespan, and optimises service contracts.
• Improved Patient Safety: Reliable equipment reduces the risk of delays or compromised care during emergencies.

6. Population Health Modeling

Digital twin technology isn’t limited to individual patients or hospitals-it also scales to entire communities. Population health digital twins, also known as epidemiological twins, simulate how diseases spread and how populations respond to different health interventions. These models integrate demographic data, mobility patterns, public health records, and environmental factors to create accurate, real-time representations of population health dynamics.

At Experion Technologies, we outline how Digital Twin Visions can be translated into practical, scalable outcomes by leveraging integrated ecosystems where Digital Twins function as living assets.

How Population-Level Digital Twins Are Used?

• Epidemiological Modelling: Digital twins simulate how infectious diseases may spread across a city, region, or country. They account for variables such as transmission rates, social behaviour, vaccination coverage, and healthcare capacity.
• Pandemic Preparedness: Governments and public health agencies use these simulations to test containment strategies, assess resource needs, and plan for worst-case scenarios-long before an outbreak worsens.
• Vaccination Strategy Planning: By modelling immunity levels, risk groups, and transmission hotspots, digital twins help officials design targeted vaccination campaigns that maximise impact.

All this leads to more accurate projections of outbreak trajectories, improved emergency response planning, data-driven public health policies, and an optimised resource allocation during crises. Population health modelling enables authorities to make proactive decisions that protect communities, reduce mortality, and enhance resilience during pandemics or large-scale health emergencies.

 

Real-World Digital Twin in Healthcare Examples

Leading Industry Applications

Digital Twins in Healthcare have been utilised in the real world in a variety of innovative ways. Here are some of the leading industry applications:

1. GE Healthcare: Medical Imaging machines, such as CT and MRI Scanning, are quiet costly. By applying the concept of Digital twins here, the hospital administration can be aware of potential failures of these machines and take the necessary preventive actions. The longevity of machines can thereby be enhanced.
2. Siemens Healthineers: Siemens Healthineers is developing cardiac digital twins. These are patient-specific virtual heart models that combine imaging, ECG data, and AI to simulate real cardiac function. These digital twins let clinicians test procedures and therapies virtually before performing them in real life, such as optimizing pacing lead placement, evaluating valve repair options, or predicting drug response. By continuously updating the model with new patient data, Siemens aims to support earlier detection and more precise treatment planning.
3. Philips: Philips is building hospital operations digital twins. These are basically virtual replicas of an entire hospital’s workflows, capacities, and patient movement, to help improve patient flow and overall operational efficiency. These twins combine real-time data from admissions, staffing, bed availability, imaging queues, and emergency department activity to simulate how the hospital functions under different conditions. Leaders can then test scenarios in these digital twins before applying them in the real hospital. By predicting bottlenecks and improving patient throughput, Philips’ operations twins help hospitals reduce wait times and prevent overcrowding keeping the entire system running more smoothly.
4. Dassault Systèmes’ Living Heart Project: Dassault Systèmes’ Living Heart Project is an initiative that uses advanced simulation technology to create a highly realistic 3D, beating digital model of the human heart. Built on the 3DEXPERIENCE platform, it integrates detailed anatomical structures, tissue properties, and electrophysiology to accurately mimic how a real heart looks, moves, and responds to interventions. Researchers and medical device companies use the Living Heart model to test implants, simulate surgical procedures, study disease progression, and design personalized treatments without ever risking patient safety.
5. Johns Hopkins University: Johns Hopkins University is developing digital twins for neurosurgery, patient-specific virtual models of the brain that let surgeons plan and rehearse procedures more safely. By combining MRI/CT data with functional brain mapping and tissue-response simulations, these twins help surgeons test different surgical paths, anticipate risks, and choose the safest approach. The goal is to improve precision, reduce complications, and make neurosurgery more personalized.

 

Benefits of Digital Twin Technology in Healthcare

The real-world examples make it clear that Digital twin technology offers wide-ranging benefits across clinical care, hospital operations, and medical research.

• Clinically, it enables more accurate diagnoses, highly personalized treatment plans, reduced trial-and-error in medication, and greater precision during surgical procedures.
• On the operational side, digital twins help hospitals lower costs, optimize the use of staff and equipment, minimize downtime, and improve patient flow by predicting bottlenecks before they occur.
• In research and innovation, they accelerate drug development by making simulations faster and more cost-effective, reducing reliance on animal testing through ethical virtual trials, and supporting dynamic modeling that leads to quicker, deeper clinical insights.

 

Challenges in Using Digital Twin Technology in Healthcare

• Data Privacy & Security

Integrating large amounts of sensitive patient data, such as EHRs, genomics, and wearables, raises major concerns about data breaches and cybersecurity attacks. Collection of such data would need to follow regulations such as GDPR and HIPAA.

• Interoperability Issues

Interoperability issues pose a major barrier to the effective adoption of digital twins in healthcare. Because many health systems operate in fragmented environments with disparate EHR platforms, IoT devices, and imaging systems, integrating data into a unified, accurate digital model becomes extremely challenging. Without standardized protocols and seamless data exchange frameworks, digital twins in healthcare cannot access the consistent, real-time information they need to function reliably, limiting their overall impact and scalability.

• High Implementation Costs

The development and maintenance of patient-specific Digital Twins are very complex and require significant investment in IT infrastructure, advanced AI models, and advanced sensors. The high cost of adoption can be a significant barrier, especially for smaller hospitals that may struggle with adoption.

• Ethical Concerns

Ethical concerns play a major role in the deployment of digital twins in healthcare. Ensuring transparency in AI-driven decision-making is important so clinicians can understand how predictions are generated. Questions around data ownership also arise, as it must be clear who controls and has access to a patient’s digital twin and how that information can be used. Additionally, the risk of algorithmic bias remains a major concern- if the underlying data or models are skewed, digital twins could unintentionally reinforce health disparities or lead to inaccurate clinical decisions.

 

Future Trends: What’s Next for Digital Twins in Healthcare?

The future of digital twins in healthcare is extremely promising, with the technology moving quickly from experimental pilots to becoming a central component of modern clinical practice. As data, AI, and computational modeling converge, digital twins will evolve from isolated use cases into fully integrated systems that support diagnosis, treatment planning, monitoring, and population health management.

Emerging Innovations

• Full-body digital twins for proactive disease prediction.
  Researchers are working toward comprehensive models that capture the entire human body, including organ interactions, metabolism, genetics, and lifestyle factors. These whole-body twins could simulate disease onset years in advance, enabling preventive interventions instead of reactive care.
• Hospital-wide digital twin ecosystems to manage resources in real time.
  Future hospitals may operate on top of live digital replicas that track patient flow, staff allocation, equipment status, and ICU capacity. These systems will enable administrators to predict bottlenecks, reduce wait times, and optimize operations minute by minute.
• Integration with genomics and wearable tech for hyper-personalized care.
  As genomic sequencing and continuous monitoring devices become mainstream, digital twins will incorporate these data streams to tailor treatments at unprecedented levels. Care plans could adapt dynamically based on genetic risk, daily vitals, and lifestyle patterns.
• AI-driven autonomous decision-support systems.
  Advanced AI models will use digital twins in healthcare to simulate outcomes and inform optimal clinical decisions. It can suggest drug adjustments, predict complications, or plan surgical strategies. Over time, this may evolve into semi-autonomous systems that continuously optimize patient care.

The Road to 2035

• Healthcare is entering an era where every patient may have a constantly updated digital replica.
  By 2035, it is realistic that most individuals will have a digital twin linked to their medical records, wearables, imaging studies, and lab data, continuously updated to reflect their actual health status.
• Could dramatically reduce global disease burden and healthcare costs.
  By enabling earlier diagnosis, targeted treatments, fewer complications, and optimized hospital operations, digital twins have the potential to significantly reduce both the chronic disease burden and the cost of care worldwide.

 

Conclusion

Digital twin technology is not just an upgrade- it is a revolution. From precision medicine to hospital optimization, digital twins in healthcare are enabling smarter, safer, and more predictive healthcare systems.

As adoption grows and technological barriers fall, digital twins will form the backbone of next-generation healthcare, supporting clinicians in delivering more efficient, personalized, and proactive treatments. The future of medicine may very well begin with a digital replica. In the coming decades, medical breakthroughs may not start in the clinic- but within the precision of a digital twin.

By bringing together insights around Digital Twin implementation, Experion Technologies help organizations envision a future-ready Digital Twin Ecosystem.

Key Takeaways

• Digital twins in healthcare provide real-time, virtual replicas of patients, medical devices, or entire hospital systems, enabling predictive, personalised, and efficient healthcare systems.
• The global digital twin in healthcare market is projected to reach USD 4 billion by 2028, driven by advancements in AI, IoT, genomics, and simulation technology.
• Key applications include precision treatment, surgical planning, operational optimisation, predictive maintenance, and population health modelling.
• Benefits include improved patient outcomes, reduced hospital costs, and enhanced decision support; however, challenges remain around privacy, interoperability, and ethical data use.
• Future innovations point toward full-body digital health twins, next-generation clinical decision systems, and hospital-wide digital ecosystems.