Creating Virtual Humans for Personalized Medicine
The future of healthcare lies not in test tubes alone, but in the digital realm where virtual copies of patients allow doctors to simulate treatments before administering them.
Imagine a world where your doctor could test dozens of treatment options on a perfect digital copy of you—predicting side effects, optimizing drug dosages, and preventing diseases before they manifest. This isn't science fiction; it's the promise of digital twin technology in healthcare.
From revolutionizing medical education to enabling truly personalized treatments, digital twins are poised to transform how we understand, teach, and practice medicine. These virtual replicas of physical entities, continuously updated with real-time data, are moving beyond manufacturing and into hospitals and medical schools, creating what experts call "the flight simulator for the human body" 6 .
A digital twin is a virtual replica of a physical object or system that mirrors its real-world counterpart in real-time or near-real-time 1 2 . In healthcare, this concept evolves into Human Digital Twins (HDTs)—dynamic virtual models of individual patients that are continuously updated with data from various sources including electronic health records, wearable devices, genetic information, and environmental factors 7 .
Think of it as a "flight simulator for the human body" 6 . Just as pilots train in risk-free virtual environments, doctors can use digital twins to test treatments and simulate biological behaviors without endangering actual patients.
The National Academies of Sciences, Engineering, and Medicine (NASEM) has established specific criteria for what constitutes a true digital twin in healthcare: it must be personalized, dynamically updated, and have predictive capabilities that inform clinical decision-making 7 . This distinguishes genuine digital twins from simpler digital models or shadows that lack real-time data exchange and predictive power.
Digital twin technology is reshaping health higher education by providing interactive, patient-safe learning environments where students can simulate procedures, model disease progression, and test interventions without risk to actual patients 1 5 .
The International Medical Informatics Association recommends including DT principles in biomedical and health informatics education alongside other emerging technologies.
Established a Centre for Doctoral Training in Digital Twins for Healthcare (DT4Health), creating a dedicated pipeline for training the next generation of experts.
The International Medical Informatics Association (IMIA) has recognized the growing importance of this field, recommending that biomedical and health informatics education now include understanding DT principles alongside other emerging technologies like blockchain, cloud computing, and the Internet of Things 1 . This educational shift requires a multidisciplinary approach that brings together computer science, information science, engineering, health sciences, and even socio-behavioral sciences 1 .
Professional training is also evolving through workshops like those offered by the Digital Twin Consortium, where technologists and healthcare professionals gain hands-on experience building and working with digital twins 8 . These practical sessions cover everything from Model-Based Systems Engineering (MBSE) to AI-driven digital twin development, ensuring the healthcare workforce keeps pace with technological advances.
A 2025 study published in npj Digital Medicine demonstrated a practical application of digital twin technology for tracking workflow in a Critical Care Unit 3 . Researchers developed a novel dual-layer architecture to monitor both physical and conceptual entities in real-time using Azure cloud services.
The methodology focused on tracking treatment workflows by analyzing observation forms, which successfully captured 72% of staff-performed tasks as they happened 3 . This approach allowed for discrete event simulation—a modeling technique that simulates processes or workflows of complex systems using state changes of activities and assets.
72% of staff-performed tasks successfully tracked in real-time 3
The implementation demonstrated that 72% of staff-performed tasks could be successfully tracked in real-time through review of observation forms alone 3 . This capability to monitor workflow efficiency and identify bottlenecks in real-time represents a significant advancement over traditional retrospective analysis methods.
The study underscored digital twins' potential to bridge the gap between actual and ideal clinical practices by providing immediate feedback on operational efficiency 3 . This real-time visibility into clinical workflows enables healthcare organizations to identify inefficiencies, optimize resource allocation, and ultimately improve patient safety outcomes in critical care settings—where timing and precision are paramount.
| Implementation Level | Description | Prevalence in Current Research |
|---|---|---|
| Full Digital Twins | Dynamic, bidirectional data flow with predictive capabilities | 12.08% of studies |
| Digital Shadows | One-way data flow from physical to virtual | 9.4% of studies |
| Digital Models | No automatic data exchange | 10.07% of studies |
| Virtual Patient Cohorts | Generalized models for patient groups | 10.07% of studies |
Data adapted from npj Digital Medicine scoping review of 149 studies (2025) 7
Creating functional digital twins for healthcare requires a sophisticated integration of multiple advanced technologies:
| Technology | Role in Digital Twins | Specific Applications in Healthcare |
|---|---|---|
| Internet of Things (IoT) | Provides real-time data through connected devices and sensors | Wearable health monitors, smart implants, environmental sensors |
| Artificial Intelligence & Machine Learning | Enables pattern recognition, predictive analytics, and decision support | Treatment outcome prediction, disease progression modeling |
| Cloud Computing | Offers scalable storage and processing power for massive datasets | Secure health data repositories, complex simulation environments |
| Blockchain | Ensures data security, integrity, and traceability | Patient record management, clinical trial data integrity |
| Augmented & Virtual Reality | Provides immersive visualization and interaction interfaces | Surgical planning, medical education, patient education |
Information synthesized from multiple research sources 2 5 9
Digital twin technology is proving valuable across multiple healthcare domains:
Digital twins enable a fundamental shift from "one-size-fits-all" medicine to truly personalized approaches 5 9 . By creating virtual replicas that incorporate individual genetic profiles, lifestyle factors, and real-time physiological data, clinicians can simulate how specific patients might respond to different treatments before ever writing a prescription 6 .
For example, a cancer patient's digital twin could simulate how different chemotherapy regimens would interact with their specific tumor biology and healthy tissues, potentially leading to more accurate dosing, fewer adverse effects, and improved outcomes 6 . In cardiology, digital twins of patients' hearts are already being used to plan surgeries, model arrhythmias, and guide implantable device selection 6 .
Beyond individual patient care, digital twins make hospitals' operational processes more resilient and efficient 1 . They can simulate patient flows, optimize resource allocation, and enhance emergency response planning through cognitive situational awareness and common operational picture (COP) development 1 .
This systems-level application became particularly valuable during the COVID-19 pandemic, when digital twins were used for hospital capacity planning and infectious disease modeling 9 . The technology allows healthcare administrators to run simulations and stress-test their systems against various scenarios, ensuring continuity of care even during crisis situations.
| Medical Specialty | Application Focus | Current Implementation Examples |
|---|---|---|
| Cardiology | Heart modeling, surgical planning, arrhythmia simulation | 28.86% of current digital twin research 7 |
| Metabolism | Diabetes management, insulin response modeling | 12.75% of current digital twin research 7 |
| Oncology | Treatment response prediction, personalized therapy | Virtual patient cohorts for clinical trials 7 |
| Critical Care | Workflow optimization, resource allocation | Real-time task tracking in ICUs 3 |
Despite their potential, digital twins face significant implementation challenges. A 2025 scoping review revealed that only 12.08% of studies claiming to create healthcare digital twins actually met all NASEM criteria for true digital twins 7 . Most implementations lacked either dynamic updating, predictive capabilities, or both.
Technical barriers include interoperability problems between siloed health data systems.
Data privacy, security, and responsibility for inaccurate predictions need resolution.
Validation standards and clinical certification pathways remain undefined.
Technical barriers include interoperability issues between siloed health data systems and the substantial computing resources required for real-time simulations 6 . Additionally, ethical questions around data privacy, security, and responsibility for inaccurate predictions need resolution before widespread adoption 6 .
The regulatory landscape for digital twins in healthcare also remains undefined, with questions about validation standards and clinical certification pathways still unanswered 2 7 . As the field matures, establishing frameworks for verification, validation, and uncertainty quantification (VVUQ) will be essential for building clinical trust 7 .
The trajectory of digital twin technology in healthcare points toward increasingly sophisticated applications. The global market for healthcare digital twins is projected to grow at a compound annual growth rate exceeding 30% between 2023 and 2027, with 66% of healthcare executives planning investments in this technology 9 .
Through continuous monitoring and intervention simulation 6
Using neurobiological digital twins for psychiatric care 6
On patient-specific digital twins before actual operations 6
With real-time data from wearables automatically updating digital twins 6
As technical and ethical hurdles are addressed, digital twins will likely become integral to predictive, preventive, and participatory healthcare 5 . The question is no longer if digital twins will transform medicine, but when they will become standard practice in clinics and hospitals worldwide 6 .
The digital twin revolution represents more than just technological advancement—it promises a fundamental shift from reactive healthcare to proactive, personalized wellness management that could extend healthy lifespans and improve quality of life for millions.