Digital Twins in Healthcare – A New Era of Innovation for Businesses

Digital twins are just some of the new technologies that are quickly transforming the healthcare sector. These virtual copies are essential to ensuring better practices in the healthcare industry, benefiting medical organizations and patient care.

For instance, Johns Hopkins University pioneered a groundbreaking digital twin technology advancement with the first FDA-approved virtual heart model. This digital twins healthcare model enables precise simulations of cardiac behavior, allowing doctors to personalize treatments and predict patient outcomes with greater accuracy.

By leveraging high-performance computing and neural networks, this innovation represents a major leap toward individualized healthcare in the United States, reducing reliance on trial-and-error methods and improving clinical decision-making.

Experts in this industry expect significant growth in 2028, when the market will be worth over $21.1 billion (Source: MarketsandMarkets).

The phenomenal exponential growth year after year is truly an amazing feat­. Moreover, reports also suggest that the digital twin technology market is set to grow by nearly 60% each year, paving the way for substantial expansion by 2027 (Source: McKinsey). Such faith in digital twins’ ability is proof of the amazing benefits these innovative technologies bring to patient care and operational management.

It should be noted that this trend is not just temporary but an absolute change in the technological influence on health management. This article will explore the importance of digital twins in healthcare, highlighting the benefits, possibilities, and challenges.

What are Digital Twins, and how are they used in Healthcare?

Digital Twins in healthcare are the computer representations of a system, from patients to hospitals and everything in between, both physical entities and processes. The digital counterparts incorporate electronic health records (EHRs), disease registries, omics data, including proteomic, genomic, metabolomic, and bionic data, and demographic and lifestyle information for each person.

The rapid advancement of healthcare IoT and AI, combined with the increasing availability of multidimensional data, including behavioral, biometric, cognitive, emotional, and psychological insights, has fueled the expansion of digital twins in healthcare. These technologies enable more precise simulations, predictive analytics, and real-time monitoring, driving more efficient and personalized healthcare solutions.

As per a report by Grand View Research, the digital twins healthcare market is growing at an unprecedented rate and is projected to reach $3.55 billion by 2030. The increasing demand for personalized medicine, predictive analytics, and the adoption of digital health solutions across the industry drive this surge.

When fully utilized, medical digital twins can revolutionize lifestyle management, wellness tracking, and chronic disease treatment. By continuously updating virtual models with real-time data from wearable devices, mobile health apps, and self-reported patient inputs, digital twins can provide deeper insights into individual health patterns.

This enhanced understanding of the role of digital twins in healthcare will enable providers to develop more accurate treatment guidelines, deliver personalized recommendations, and ultimately improve patient outcomes on a large scale.

Types of Digital Twins in Healthcare

The use of digital twin in the healthcare industry can be categorized based on their purpose and the level of complexity involved. These models range from patient-specific simulations to large-scale hospital system replicas, each serving distinct healthcare needs. Here are the main types:

Patient/Person or Body System Digital Twins (Autonomous Digital Twins)

Patient digital twins create a virtual model of an individual’s health profile by integrating data from electronic health records, wearables, and imaging technologies. These digital replicas enable precision medicine, chronic disease management, and proactive healthcare interventions.

Surgical Digital Twins

Surgical digital twins revolutionize preoperative planning by allowing surgeons to simulate complex procedures in a virtual environment. This technology is particularly beneficial for robot-assisted and minimally invasive surgeries, enhancing precision and patient safety.

System Digital Twins

System digital twins replicate healthcare infrastructure, including hospital networks, medical devices, and supply chains, to enhance operational efficiency. These models are instrumental in predictive maintenance, preventing costly equipment failures before they happen.

Cellular & Molecular Digital Twins

Cellular and molecular digital twins are transforming drug discovery and genomics by simulating cellular behavior and treatment responses at a microscopic level. Researchers use these models to study disease progression and test drug efficacy before clinical trials, making digital twin medicine a game-changer in personalized treatment development.

Process Digital Twins

Process digital twins streamline hospital workflows, optimize patient flow, and improve overall healthcare logistics. These models help administrators identify inefficiencies and enhance resource allocation.

In emergency departments, for instance, a digital twin hospital can simulate various patient arrival scenarios, enabling hospitals to optimize triage processes, reduce wait times, and enhance real-time operational efficiency.

Organ or Smaller Unit Digital Twins

Organ digital twins replicate specific human organs or body parts to simulate and analyze how they respond to medical interventions. These models are crucial in personalized treatment planning, particularly cardiology, nephrology, and gastroenterology.

Population Health Digital Twins

Population health digital twins analyze large-scale health data to predict disease outbreaks, enhance epidemic response strategies, and optimize public health policies. Governments and healthcare organizations use these models to simulate virus spread, plan vaccination campaigns, and allocate medical resources effectively.

During the COVID-19 pandemic, digital twins played a key role in forecasting infection trends and supporting data-driven decision-making for public health management.

Progressive Digital Twins

Progressive digital twins rely on data through time to create an intelligent learning system demonstrating how a physical entity (such as a body) transforms and forecasts potential future outcomes. They resemble digital versions of individuals that illustrate changes in molecules’ bodily processes and conditions (like aging) as time passes.

For example, researchers are developing three-dimensional brain cell models in laboratories to examine how the human brain operates and replicate disorders like Alzheimer’s, ALS, and microcephaly.

Static Digital Twins

A static twin resembles a standard simulation where experts examine things without live connections. It relies on math based on estimates to work things out. This static twin stems from information gathered by sensors that monitor a real system. In essence, static twins are mathematical models created from patient data.

For example, Philips developed a static twin called HeartNavigator.

Key Technologies Used in Digital Twins for Healthcare

Various technologies are used to gather patient data when developing and deploying Digital Human Twins (DHTs). Digital health sensors and devices gather information directly from the patient and/or surroundings, and afterwards, they transmit and store it in real time in the IoT cloud. Here’s the list of the key technologies used in Digital Twins:

Use Cases of Digital Twins in Healthcare

The digital twin applications in healthcare cover many areas within patient care and hospital/medical services. Digital twins are becoming increasingly popular, and this section identifies important applications that help improve medical services and patient care.

Clinical Trials and Drug Discovery

The use cases of digital twins in healthcare present a novel way of dealing with the ethical and logistical problems surrounding clinical trials and drug development. These virtual models simulate complicated biological processes and responses of the human body.

Researchers can use these models extensively to test possible treatments and pharma compounds. It speeds up the trial process while reducing risks from conventional human or animal trials, making drug development approaches safer and quicker.

Patient Remote Monitoring

Digital twins have become an invaluable tool for monitoring the patient’s well-being at a remote distance and providing appropriate care or treatment for patients. Such capability is especially useful for people living in remote areas and those with mobility difficulties. In other words, with these virtual versions, doctors can always check live data to determine if there are any deviations from the normal to take timely measures and implement a preventive approach in medicine.

Customized Medicine

The medical digital twin is one of the most advanced approaches to patient care. Through the exploitation of a lot of patient-specific information, these digital replicas are used to determine a personalized mode of treatment that considers one’s unique health conditions, genetics, way of life, and particular medical requirements.

This individualized approach ensures high treatment efficiency and reduces the risk of adverse reactions or ineffective measures. Thus, using digital twin medicine contributes to gaining positive results and improving the quality of life for patients.

Surgery Planning

Implementing digital twins in healthcare is essential to revolutionizing surgical planning and readiness. By creating fine 3D models of the particular anatomical structure of individual patients, healthcare professionals can precisely imagine and perform virtual surgical procedures, foreseeing and minimizing difficulties and maximizing surgical plans.

Such detailed pre-operative planning helps reduce complications, shorten operating times, increase surgical accuracy, and improve postsurgical recuperation.

Epidemic Management

The impact of digital twins in the healthcare industry is evident in controlling the spread of pandemics like COVID-19. By leveraging vast datasets including social gathering patterns, population density, and healthcare facility availability, digital twins empower medical professionals to make data-driven decisions. They help optimize resource allocation and implement effective containment strategies.

Medical professionals can use this data to analyze the various factors that influence the spread of the disease and take proactive measures to control the potential spread of infectious diseases by adjusting different parameters.

Prosthetics and Implants

Developing and fitting prosthetics and implants to individual patients with special anatomical needs presents a new approach that utilizes digital twins to ensure optimal fit and functionality.

For example, by making digital duplicates of patients’ injured body parts, healthcare professionals will design prosthetics and implants that are highly accurate in relation to each patient’s specific anatomical structures, ensuring the patient’s comfort and mobility.

These digital representations can also be applied to simulate post-procedure movements and rehabilitation exercises, which helps physiotherapists prescribe personalized rehabilitation programs to speed up recovery and improve patients’ lives.

Medical Device Development

Digital twinning in medical device development provides complete virtual testing and research. By leveraging a medical digital twin, developers can evaluate the effects of design variations, test different operating conditions, and foresee possible malfunctions before proceeding with the fabrication of actual units. These virtual facsimiles of medical instruments enable faster innovation, reduce development costs, and enhance device reliability, ultimately leading to safer and more efficient healthcare solutions.

This proactive approach is cost-effective and risk-reducing for product failures in healthcare. Also, it shortens the time needed to market these life-saving products. Digital twins implementation in healthcare allows developers to deliver safe and high-performing devices that are compliant with strict safety standards, ensuring improved patient care and healthcare outcomes.

Hospital Operations

Digital twin hospital technology facilitates the development of virtual versions that can be used to assess and optimize different elements of healthcare administration. Hospital managers can leverage digital twins to evaluate resource effectiveness, improving bed management, staff scheduling, and operating room utilization.

In addition, the features of the digital twin technology in healthcare provide a safe ground for assessing the effects of changes on systems’ performance and make it possible to adopt preventive measures in the hospital environment. Hospitals can simulate possible operating conditions to make advanced plans for risk prevention.

Advanced Digital Twin Use Cases in Healthcare

As digital twin technology continues to evolve, its applications in healthcare are expanding beyond traditional uses. These advanced implementations drive innovation in patient care, treatment planning, and operational efficiency. Let’s have a look at those.

Bio-Manufacturing

Facilitates precise modeling of biological processes for drug development, tissue engineering, and regenerative medicine. This accelerates production while ensuring product safety and quality.

Individualized Homeostasis Monitoring

Continuously tracks physiological data to detect imbalances early, enabling timely intervention. This personalized approach helps maintain stability in a patient’s health.

Cancer Management

Develops virtual tumor models to analyze treatment responses, allowing for more precise and personalized cancer therapies. This approach enhances treatment effectiveness and improves patient outcomes.

Cardiovascular Disease

Creates digital heart models to study disease progression, predict risks, and personalize treatments. This technology aids in early diagnosis and more effective intervention strategies.

Immune Responses

Simulates the body’s immune reactions to infections, vaccines, and treatments, enabling better disease management. This helps optimize therapies for autoimmune and inflammatory conditions.

Real-World Examples of Digital Twins in Healthcare

Digital twins in healthcare have seen massive utilization across various medical operations and functions, making a significant impact. Here are the top real-world examples of digital twins in healthcare, showcasing innovative approaches transforming patient care and healthcare administration. From personalized treatment planning to hospital resource optimization, this technology is reshaping the future of medicine.

1. Babylon Digital Twin for e-medical Records

Babylon Digital Twin is the leading platform that helps individuals see the whole picture of their medical records and other health information. The system provides essential information regarding organ status and predisposing factors by employing advanced AI technology coupled with sophisticated deep learning techniques.

Currently, the platform focuses on only 20 diseases, but its expansion to capture more than 80 diseases is anticipated. Babylon’s Digital Twin confirms the increasing role of individual health data that enables people to stay on top of their health and well-being.

2.  Hospital Ward Management Digital Twin at Mater Hospital, Dublin

Mater Hospital Dublin is an example of the transformative nature of healthcare digital twins that enhance the smooth functioning of hospitals and healthcare facilities. Mater Hospital Dublin utilized 3D digital replicas to critically evaluate its daily ward operations to improve the radiology department. As a result, a significant reduction in patient wait time for critical CT and MRI scans marked a real improvement in patient experience under the digital twin initiative.

Integrating workflow-simulated digital twins at the Mater Hospital led to a decrease of about 240 minutes in the wait time for CT or MRI scans. Such optimization contributed to shorter patient turnaround times, increasing MRI and CT scan capacities by 32% and 26%, respectively.

3. Mayo Clinic’s Digital Twin for Precision Cancer Treatment

Mayo Clinic is leveraging digital twins to develop patient-specific tumor models, enabling oncologists to simulate different treatment options before administering them. This personalized approach minimizes trial-and-error in cancer therapy, leading to more effective treatments and better patient outcomes.

By integrating AI and real-time patient data, the application of digital twins in healthcare can predict how a tumor will respond to various therapies, allowing doctors to refine treatment strategies and avoid unnecessary side effects. This innovation enhances precision oncology, improving survival rates and patient quality of life.

4. Stanford University’s Digital Twin for Cardiovascular Research

Stanford University has developed digital twin models of the human heart to simulate and study cardiovascular diseases. These models help cardiologists assess the impact of different treatment strategies, leading to more precise and effective interventions.

By integrating patient-specific data from imaging, genomics, and wearables, these digital twins provide a deeper understanding of heart conditions, allowing for tailored treatment plans. Researchers have used these models to simulate the effects of heart implants and medications, reducing the need for invasive procedures while improving long-term cardiac health.

5. GE Healthcare’s Digital Twin for Predictive Equipment Maintenance

GE Healthcare uses digital twins to monitor the real-time performance of medical imaging equipment, such as MRI and CT scanners. Hospitals can predict failures by analyzing data before they happen, ensuring proactive maintenance, minimizing downtime, and improving patient care.

This approach extends the lifespan of critical medical devices and helps reduce operational costs by preventing unexpected breakdowns. Hospitals leveraging GE’s digital twin technology have reported significantly reduced equipment-related disruptions, ensuring uninterrupted patient diagnostics and treatment.

6. Imperial College London’s Digital Twin for Advanced Surgical Planning

Imperial College London has pioneered using digital twins for complex surgeries, including brain and orthopedic procedures. By generating patient-specific 3D models, surgeons can simulate procedures in a virtual environment before operating on real patients.

This approach enhances surgical accuracy by allowing healthcare professionals to refine techniques, anticipate complications, and optimize strategies. As a result, digital twin technology significantly improves patient outcomes, reduces surgical risks, and shortens recovery times.

7. Roche’s Accu-Chek Digital Twin for Smart Diabetes Management

Roche’s Accu-Chek integrates digital twin technology with IoT-enabled glucose monitors to help diabetics manage their condition more effectively. By continuously collecting real-time patient data, AI-powered digital twins predict glucose fluctuations and recommend personalized insulin dosages.

This approach has improved patient adherence to treatment and reduced the risk of complications associated with diabetes, such as cardiovascular disease.

Our experts at Appinventiv worked on DiabeticU, a smart diabetes management app, designed to give users greater control over their health. With personalized tools and seamless integration with wearable devices, it enables effortless monitoring and proactive management of diabetes.

This innovative application has made diabetes management more accessible, efficient, and effective. By integrating real-time data with personalized health insights, DiabeticU has revolutionized diabetes management, improving health outcomes and a better quality of life for users.

Advantages of Digital Twins Technology in Healthcare

The usage of digital twins in healthcare is widespread. It can even create a digital model of the human body. With that said, here are some key advantages associated with the implementation of digital twins in healthcare:

Enhanced Patient Care

A doctor could simulate treatments for the patient using their digital twin, helping make important decisions before physically carrying out treatments on the person. Such an approach aims to make procedures safer while raising the standard of care.

Predictive Maintenance

Digital twins could be used to predict failure scenarios and perform predictive maintenance of key medical devices in healthcare. This will enable healthcare providers to track device performance continuously to foresee any breakdowns that would otherwise occur during crucial medical work processes.

Research and Development

Digital twinning serves as an important instrument in the course of medical investigations. These virtual replicas act as platforms for carrying out a host of research experiments and probing into the intricacies surrounding genetic disorders. This can go a long way in transforming healthcare approaches and medical treatment procedures, especially for rare and complex cases.

Optimization of Healthcare Systems

Digital twins help healthcare facilities pinpoint operational inefficiencies and bottlenecks in their systems. Through this proactive analysis, managers can maximize resources, thus enhancing patient flow and service delivery.

Proactive Maintenance of Medical Equipment

Digital twins in healthcare enable continuous monitoring of critical medical devices, predicting potential failures before they occur. This allows for timely maintenance, reduces equipment downtime, and ensures seamless medical procedures without unexpected disruptions.

Immersive Training & Medical Education

Medical students and healthcare professionals can gain hands-on experience through digital twin simulations, replicating complex surgical procedures and human anatomy.

This interactive learning approach enhances skill development, reduces medical errors, and refines surgical precision.

Advancing Medical Research & Drug Discovery

Digital twins serve as a virtual testing ground for medical research, enabling scientists to model disease progression, analyze genetic disorders, and simulate drug interactions. This accelerates the development of new treatments, particularly for rare and complex conditions.

Early Disease Detection & Prevention

With continuous monitoring and predictive analytics, digital twins can detect early signs of diseases before symptoms appear. This proactive approach allows for timely interventions, reducing complications in chronic conditions such as diabetes, cardiovascular diseases, and cancer.

Also Read: How can digital twin technology help your business grow?

Challenges of Adopting Digital Twin Technology in Healthcare

Digital twins in healthcare present several challenges that must be addressed with caution when implemented at every step. Here are some of the key challenges of digital twins in the healthcare industry:

Data Security and Privacy Concerns

Digital twin in the healthcare industry relies on vast amounts of sensitive health data, making security a top priority. Protecting this information from breaches or unauthorized access is critical to maintaining patient trust and regulatory compliance.

Data encryption plays a crucial role in safeguarding patient confidentiality and is a mandatory requirement under HIPAA in the U.S. and GDPR in Europe. Implementing robust cybersecurity measures ensures that digital twin technology can enhance healthcare without compromising data integrity or privacy.

Also Read: HIPAA Compliant App Development: A Complete Guide

Data Accuracy and Completeness

Accuracy and completeness in data input are among the main issues concerning digital twins. To get a complete and accurate medical history, genetic information, lifestyle factors, and even real-time health data gathered, one must be very careful about details. This involves a sophisticated data collection system.

Interoperability Challenges

This is one important problem with linking, comparing, and mixing up health data from different sources without interoperability. It promotes data interoperability and, therefore, standardizes data formats and harmonizes other data systems to exchange accurate and timely data between different healthcare platforms.

Scaling Implementation

Digital twins for health applications will confront several unique scaling problems from the pilot phase to widespread adoption in various health service environments. The healthcare digital twins pose key questions on managing data integration complications, complying with existing regulatory mechanisms, and requiring standard practices and approaches. Creating collaborative partnerships and a culture that embraces innovation and flexibility is mandatory to realize the maximum benefits of digital twins in healthcare fully.

Ethical Considerations

Adopting AI-driven digital twins in healthcare brings critical ethical and legal concerns, including patient consent, data ownership, algorithmic bias, and liability in medical decision-making. Clear ethical guidelines must ensure responsible implementation, prioritizing transparency, fairness, and accountability in digital twin technology.

Computing Infrastructure

Digital twins require high-performance computing, cloud storage, and real-time processing, which many healthcare facilities lack. Edge computing and federated learning can help reduce costs and improve efficiency.

Business Models and ROI Uncertainty

High implementation costs make ROI assessment challenging. Sustainable business models, value-based care, and strategic partnerships are key to balancing innovation with cost-effectiveness. Demonstrating long-term benefits through pilot programs and real-world case studies can help justify investments.

The Future of Digital Twin Technology in Healthcare

The future of digital twin technology in healthcare will bridge the gap between virtual modeling and real-world patient care. This innovation will enhance decision-making, streamline treatments, and redefine personalized medicine by creating intelligent, data-driven replicas of individuals and systems. Let’s look at the key future trends of digital twins in healthcare.

AI-Driven Surgical Digital Twins with Haptic Feedback

Surgical digital twins will incorporate haptic feedback, allowing surgeons to feel tissue textures and resistance in virtual simulations. This will improve robotic-assisted surgeries and precision in complex procedures.

Pandemic Simulation & Biothreat Preparedness

Governments and healthcare institutions will use population-scale digital twins to simulate pandemic outbreaks and vaccine responses. AI-driven models will optimize public health strategies and emergency resource allocation.

Digital Twin-Powered Metaverse for Virtual Healthcare

Digital twin in healthcare will merge with the metaverse to create virtual clinics where doctors and patients interact in immersive 3D environments. This will enhance AI-driven remote consultations, diagnostics, and even virtual surgeries.

Predictive Maternal & Neonatal Digital Twins

AI-powered digital twins will monitor fetal development and maternal health to predict preterm birth risks, genetic disorders, and complications. This will allow for early interventions and personalized prenatal care.

Mental Health Digital Twins for Cognitive Therapy

AI-powered digital twins will track emotional states, stress levels, and neurological biomarkers to provide personalized therapy recommendations. These models will enhance mental health monitoring and AI-assisted cognitive mental therapy.

Genome-Twin Integration for Predictive Genomics

Digital twins will integrate full genetic blueprints to predict disease risks, drug responses, and hereditary conditions. This will enable preventive healthcare strategies tailored to an individual’s genome.

Let Appinventiv Be Your Partner in Digital Twin Adoption

The development of digital twins in healthcare is a game-changing technology that can transform how patients are cared for while enhancing operational effectiveness and various other processes.

Appinventiv, as industry leaders in healthcare mobile app development services, is at the forefront of this transformation, helping healthcare providers, med-tech companies, and startups integrate AI-driven digital twin solutions for enhanced patient care and streamlined operations.

With a strong track record in healthcare innovation, we have been successfully involved in different healthcare platforms like Health-e-People, a unified health data management platform, and Soniphi, an AI-powered bioacoustic wellness app.

Beyond our expertise in healthcare app development, we specialize in digital twin adoption, helping businesses optimize efficiency, ensure compliance, and deliver superior patient outcomes in an increasingly data-driven world.

Ready to bring the power of digital twins into your healthcare system?

Let’s turn innovation into impact. Connect with our experts today and take the next step toward smarter, data-driven healthcare.

FAQs

Q. What is a digital twin in healthcare?

A. A digital twin technology in healthcare creates digital representations of real things or processes, such as patients, organs, and whole hospital environments. The process is done with the help of other modern technologies like IoT and AI.

Q. What is an example of digital twinning in healthcare?

A. An instance of the usage of digital twins in healthcare is building a digital duplicate of a patient’s body, including health data, lifestyle data, and real-time tracking data. This virtual mirror helps forecast and control health problems, provides personalized treatment programs, and delivers preventive healthcare measures.

Q. How is a digital twin used in healthcare?

A. Healthcare digital twins have various uses, such as simulated patient treatment, doctors’ training, optimization of the health system, maintenance of medical instrumentation, and research and development in medicine. The other purpose they serve is better surgery planning and disease modeling, hence better epidemic administration.

Q. What are virtual twins in healthcare?

A. A virtual twin is primarily a high-fidelity simulation used for visualization, training, and scenario planning. While it accurately represents a healthcare entity, it does not always incorporate live data updates like a digital twin.