The concept of a “digital twin” may sound like science fiction, but it’s becoming a reality in the field of medicine. Digital twins technology enables doctors to use computer models to test treatments and simulate behavior without putting people or animals at risk, and also personalize care at a level previously inaccessible to most.
Data-driven virtual twin models could redefine how doctors diagnose, treat, and monitor patients. This article examines digital twins healthcare technology, its advancements, and how it’s driving a shift toward proactive, individualized medical care.
What Are Digital Twins in Healthcare?
A digital twin is a virtual replica of a physical object, process, or system that can be used for simulation and analysis. When speaking specifically about healthcare, a digital twin is a virtual patient model. Built using personal medical data, virtual patient models provide a highly detailed, real-time simulation of a person’s body, how it’s functioning, and individual behavior.
You can imagine a digital twin in healthcare as a flight simulator for the human body. Just as pilots use simulators to train without risk, doctors can use digital twins to test treatments or predict outcomes without exposing the real patient to potential harm.
The Rise of Virtual Patient Models
The increasing availability of patient data from wearable technology, along with the leaps and bounds made in artificial intelligence, have created the conditions for virtual patient models to become mainstream. Advanced algorithms and machine learning also play a critical role in mapping healthcare data to create a functional digital twin of a patient.
Virtual patient models are built and consistently updated with patient-specific information from a variety of sources: genetics, electronic health records, lab results, imaging, and wearable health tech data. By mirroring the biological processes of a real person, digital twins allow healthcare professionals to simulate disease progression, test treatment responses, and optimize medical interventions, before applying them in real life. Digital twin simulations could, for example, predict how a heart condition might evolve or how a tumor might respond to a new drug.
And because healthcare digital twins are evolving models that adapt as more data becomes available, they are a powerful tool for personalized medicine. Clinical trials and drug development are another promising use for these virtual patient models. Pharmaceutical companies can use digital twins to model treatment outcomes across populations, reducing the need for early-stage human testing and speeding up the drug discovery process.
Personalized Medicine Powered by Simulation
While the use of digital twins is gaining traction in clinical environments, the biggest impact could eventually be in personalized medicine. Instead of prescribing generalized treatment plans, clinicians could use digital twin healthcare simulations to model various treatment options, then compare outcomes and side effects virtually. This approach would allow doctors to select the best medical intervention with minimal risk to the patient.
For example, the digital twin of a cancer patient could simulate how different chemotherapy regimens would interact with their tumor and healthy tissues. This can lead to more accurate dosing, fewer adverse effects, and improved outcomes. In cardiology, digital twins of patients’ hearts are already being used to plan surgeries, model arrhythmias, and guide implantable device selection.
Digital twins enable physicians to explore different care paths through patient-specific treatment planning, making healthcare safer, more effective, and more efficient. And patients benefit too. With better insights into their condition, greater involvement in decisions, and reduced trial-and-error treatments, personalized medicine empowers patients with more knowledge and choice.
Integration Challenges and R&D Potential
While interest and research into digital twins healthcare is growing, the integration of virtual patient models into hospitals and clinics is still in the early stages. Are digital twins being used in healthcare today? Yes, but use is currently limited to research centers and advanced clinical settings due to the following challenges:
Technical Barriers
Hospitals face significant infrastructure and interoperability issues when it comes to digital twins. Health data often lives in siloed systems across different clinics, hospitals, labs and doctors’ offices. This makes integrating all the health data of a single person into a unified, real-time virtual patient model a complex task. Many institutions also lack the computing resources needed to run real-time healthcare simulations.
Data Privacy and Ethics
The use of digital twins requires handling large amounts of sensitive health data. Ensuring patient privacy, securing data transfer, and complying with regulations like GDPR and HIPAA are all major concerns. And then there’s the undefined ethics of using virtual patient models. For example, who is responsible if a digital twin makes an inaccurate prediction that influences care negatively? It’s a new frontier of medicine that requires a new set of guidelines.
Costs and Accessibility
Currently, creating a digital twin requires a lot of resources. Building and maintaining these models takes time, expertise, and financial investment that is out of reach for many hospitals and clinics. Wider adoption will depend on clear cost-benefit outcomes being demonstrated and the development of scalable solutions.
Still, the medical world is very excited about digital twins. Pharmaceutical companies such as Sanofi and engineering companies such as Siemens have developed digital twins to help identify promising drug candidates and reduce the cost of failed trials.
There’s also growing interest in cross-sector collaboration. By combining the expertise of technologists, clinicians, and regulatory bodies, the medical community can build a strong ecosystem for digital twins in healthcare.
What’s Ahead for Digital Twins in Personalized Care?
In the future, the use of digital twins in healthcare is likely to expand across the following areas:
Chronic disease management: Patients with conditions such as diabetes, hypertension, or heart disease could benefit from personalized simulations to guide ongoing care.
Surgical training: Surgeons could practice complex procedures on a patient’s digital twin before performing the actual operation.
Mental health modeling: Research is underway to use digital twins for neurobiological modeling in psychiatric care.
Home care integration: Real-time data from wearable health tech and mobile health apps could automatically update digital twins, creating a continuous feedback loop between the home and hospital.
Progress is already being made using digital twins, including by companies specializing in virtual reality simulations, real-time health detection, and accelerating diagnostics through AI technology. Learn more about these medical breakthroughs by watching the 2025 VivaTech session “Revolutionizing Healthcare: The Future of MedTech with AI and Advanced Technologies.”
Digital twins in healthcare offer patients the promise of smarter, safer, and more efficient healthcare. Digitals twins are also part of a wider shift in healthcare from a primarily reactive treatment model to predictive, personalized medicine. As technical and ethical hurdles are tackled, the question isn’t if digital twins will be adopted, but when.
Want to learn more about how technology is shaping the future of healthcare? Read this next: The Role of AI in Healthcare: Innovation, Efficiency, and Human Impact
