Healthcare is undergoing a transformational shift away from a one-size-fits-all model to highly personalized care. A powerful force driving this change is digital twin technology, in which virtual patient models are created to replicate the biology, conditions, and potential responses of a real person. These digital twins allow clinicians and researchers to test treatments, predict outcomes, and improve patient care plans before they start.
This article explains what a digital twin is, gives real-world examples of how virtual patient models work, and shows you how digital twin technology can reduce costs while improving patient outcomes.
What Are Virtual Patient Models?
In healthcare, a virtual patient model is essentially a computer-generated copy of a patient’s body, or part of it. These models are built using real-world medical data such as imaging scans, genetic profiles, vital signs, and health history. Virtual patient models are powered by digital twin technology, which was pioneered in aerospace and engineering to test equipment.
The initial concept of a “digital twin” was developed by NASA in the 1960s. While it wasn’t called a digital twin at the time, a “living model” of the Apollo mission was created to simulate the failed Apollo 13 mission and evaluate the next steps. This was the first time a dynamic, digital twin model was ever used.
Today, when applied to medicine, virtual copies of bodies become a kind of living laboratory. A cardiologist can simulate how a heart condition might evolve under different treatment scenarios, or an oncologist can test how a patient’s tumor may respond to a specific drug. And digital twin models evolve over time, updating as new patient data is collected, so they consistently mirror a person’s actual health.
“The digital twin is, for me, the future of medicine,” Stephanie Allassonnière, an influential AI and medical researcher, told VivaTech. She contributes research to the MEDITWIN project, which develops virtual twins for medical practice. “We are trying to create a digital twin of organs, and eventually a digital twin of the patient, with clinical imaging and genetic data. So this digital twin will help test different drugs, for example, and see which is the better one.”
From Simulation to Diagnosis: Practical Use Cases
One of the most powerful applications of digital twin patient care is in healthcare simulation. Digital twins are practical medical simulation tools that can inform high-stakes healthcare decisions.
Here are three examples of how digital twin technology can be used in medicine:
Surgical Planning: VR simulators are already proven to enhance surgical skills and with digital twins surgeons can rehearse complex procedures on a virtual replica of the actual patient. While still largely in the experimental stage, this promising use is expected to minimize surprises in the operating room and improve patient safety.
Diagnostic Accuracy: Artificial intelligence technology has shown to be highly accurate at diagnosing and identifying diseases. By integrating AI, digital twins can reveal patterns in symptoms or imaging scans that might otherwise go unnoticed, helping physicians catch conditions at an earlier stage.
Predicting Complications: Instead of monitoring for the effects of treatment while it's in progress, doctors can use digital twins to visualize patient disease progression and simulate different treatment outcomes in advance. By simulating how a patient’s lungs might react to ventilation strategies, or modeling how a pregnancy is advancing, doctors can adjust care in advance and prevent harm.
A high-profile example of healthcare digital twins is Dassault Systèmes’ Living Heart Project. The French multinational has taken its decades of experience in 3D product design and simulation into the world of virtual patient models. The Living Heart Project developed the first virtual twin of a human heart a decade ago, and today uses virtual patient heart models to test medical devices and optimize treatments for cardiovascular disease.
These scenarios show how healthcare simulation tools can serve as both training resources and diagnostic tools. Digital twins can reduce trial-and-error approaches and give clinicians more confidence in their decisions.
Personalized Treatment Through Digital Twins
Perhaps the biggest promise of digital twin technology in healthcare is in its ability to tailor care to individual patients. No two patients are exactly alike, and treatments that work for one person may fail for another, but medical care today is often one-size-fits-all. Virtual patient models can change this by allowing for precise, data-driven customization.
Examples of how digital twins enable personalized treatment include:
Personalized Care Pathways: By simulating how different drugs, therapies, or surgical strategies might work on a specific patient, doctors can choose the care path most likely to succeed.
Predictive Analytics: Algorithms plugged into digital twin models can forecast immediate outcomes and long-term effects, such as whether a patient is likely to relapse or develop side effects.
Chronic Disease Management: For conditions like diabetes or heart disease, ongoing updates to a patient’s virtual twin can provide real-time insight into how lifestyle or medication adjustments are affecting their health outcomes. In type 2 diabetes patients for example, intensive glucose management enabled by digital twin technology has shown the potential to reduce medication dependence and improve overall health.
A secondary, but also important effect of this personalization is that it empowers patients with knowledge about their care. When patients see how their digital twin responds to treatments, they can feel more engaged and confident in following the care plan outlined by their medical team.
Cost and Outcome Advantages
Healthcare systems around the world are under pressure to improve outcomes while reducing costs. Can digital twins improve patient care while also providing cost-effective care? Evidence suggests they can through:
Reducing Trial-and-Error Treatments Instead of cycling through different medications until something works, physicians can use a digital twin to predict the best option from the start, sparing patients from ineffective or harmful treatments.
Avoiding Costly Complications By predicting how surgeries or treatments might unfold, digital twins reduce the risk of errors, hospital readmissions, and extended recovery times – all of which drive up a patient’s healthcare costs.
Scalable Implementation If more hospitals adopt medical simulation tools, the cost of creating and maintaining digital twins will fall. This would make the technology more accessible to smaller clinics.
For health systems, the use of digital twins could lead to fewer wasted resources, better patient experiences, and improved outcomes at scale. All wins for medical professionals and patients alike.
The Road Ahead
The trajectory for digital twins in healthcare points to rapid growth. A market research study published in 2025 by Custom Market Insights predicts that the Global Healthcare Digital Twins Market will grow from about .2 billion in 2023 to nearly 5.1 billion by 2033.
But there are several challenges to this growth, including the ethical and privacy concerns that come with integrating sensitive health data into virtual patient models. Gathering all of the data necessary to create an accurate digital twin model – medical records, sensor data, scans, genetic information, and more – can be especially challenging in countries without a national medical records database.
Still the potential is clear. Digital twin technology can reshape medicine by turning patient data into powerful, living simulations. For medical professionals, virtual patient models mean better surgeries, fewer complications, and personalized treatment pathways. For patients, digital twins can lead to more accurate diagnoses, fewer failed treatments, and greater confidence in their care journey.
Want to learn even more about how digital twins can change your medical care? Read this next: Digital Twins in Healthcare: The Future of Personalized Medicine
