Digital twin technology, once considered a futuristic idea, is rapidly becoming a practical reality in modern healthcare. Today, digital twins in healthcare are being explored across a range of specialities, supporting more advanced predictive analytics in healthcare and helping clinicians refine personalised medicine approaches. These emerging healthcare technology tools are now being tested or implemented in major specialities, demonstrating how digital twins are beginning to reshape clinical decision-making and patient care pathways.
For healthcare professionals, the potential impact is significant. Digital twins may support safer treatment decisions, improve personalisation, reduce trial-and-error prescribing, and even help anticipate risks earlier in the care cycle. These virtual patient models use data from electronic health records, imaging, laboratory values, genomics, and even wearable devices to create highly detailed digital representations of individual patients.* Clinicians can then simulate disease progression, predict complications, or test how different treatment strategies might work before applying them in real life.*
For example, one study of more than 1,800 people with type 2 diabetes found digital twin technology reduced HbA1c levels by 1.8%, with nearly 90% of patients reaching their target glucose control. With the global market for digital twins in healthcare expected to grow from $902.6 million in 2024 to $3.55 billion by 2030, clinicians are increasingly encountering this technology in everyday practice.*
In this article, we discuss:
• What Are Digital Twins in Healthcare?
• Potential Benefits and Clinical Use Cases of Digital Twins
• Challenges to Adoption and Clinical Readiness
• How Clinicians View Digital Twin Adoption
• Future Outlook for Digital Twins In Healthcare and Personalised Medicine
What are Digital Twins in Healthcare?
Digital twins in healthcare are advanced, data-driven virtual representations of individual patients, designed to reflect how a person’s body functions in real time. Unlike traditional clinical simulations, digital twin technology creates a dynamic model that evolves as new clinical information becomes available. This allows clinicians to explore treatment options, anticipate risks, and better understand disease progression through dynamic modelling and predictive analytics in healthcare.
To build these virtual patient models, multiple data sources are combined, like electronic health records, imaging, pathology results, genomics, and continuous inputs from wearable devices. Algorithms and physiological modelling techniques then transform this information into virtual patient models that closely mirror each patient’s biological systems. These models can be used to test different medications, adjust dosing strategies, or assess the potential impact of lifestyle changes, creating a safer and more informed decision-making process.
Today, digital twin technology is being explored across a range of clinical fields. In cardiology, digital twins can simulate heart structure and electrical activity to support personalised treatment planning. In diabetes care, they help predict responses to therapy and optimise glucose control. In neurology, particularly epilepsy, digital twins are being used to model seizure dynamics and support therapy adjustments. Early research in oncology is investigating how tumour-specific digital twins might improve treatment targeting and predict therapeutic outcomes.
As healthcare technology continues to advance, the potential for personalised medicine grows. Digital twins offer clinicians a sophisticated tool to tailor care more precisely while reducing uncertainty and improve clinical decision-making. While the technology is still evolving, its early applications highlight a promising shift towards more predictive and patient-specific models of care.
Potential Benefits and Clinical Use Cases of Digital Twins
The growing use of digital twins in healthcare is reshaping how clinicians plan treatments, manage chronic conditions, and anticipate complications. One of the clearest advantages is improved personalised medicine. Because virtual patient models incorporate data unique to each patient, including imaging, biomarkers, comorbidities, lifestyle factors, and treatment history, clinicians can compare several therapeutic strategies without exposing the patient to unnecessary risk. This reduces the trial-and-error approach that often slows treatment optimisation, particularly in complex chronic conditions.
In cardiology, for example, digital twin technology is being used to model cardiac function and predict how patients might respond to heart failure therapies or interventions for arrhythmias. These simulations, combined with predictive analytics in healthcare, help clinicians identify the most effective strategy earlier, potentially reducing hospital admissions and improving outcomes.
In diabetes care, digital twins support more accurate glucose prediction and therapy adjustments, helping clinicians understand how medication changes may affect HbA1c levels over time. Early studies show meaningful improvements in glycaemic control when virtual patient models are integrated into routine care. *
These benefits also extend to workflow efficiency. By providing clinicians with immediate insights from simulated scenarios, digital twins support faster decision-making, reduce unnecessary investigations, and help allocate resources more effectively. As healthcare technology continues to evolve, virtual patient models may become essential tools for enhancing safety, reducing variation in care, and supporting evidence-based clinical decisions.
Challenges to Adoption and Clinical Readiness
• Data quality and consistency – Virtual patient models depend on accurate, comprehensive data from EHRs, imaging, pathology, and wearables, but many healthcare systems still struggle with fragmented information and limited interoperability.
• Workflow integration challenges – For digital twins to be useful in clinical practice, they must integrate seamlessly into existing workflows without adding to clinicians’ workload, something many current tools have yet to achieve.
• Cost and resource limitations – Implementing digital twin technology requires specialised technical expertise and long-term investment, which can be difficult for smaller organisations to support.
• Ethical and governance concerns – Data privacy, informed consent, and transparency of predictive algorithms remain significant considerations, especially as personalised medicine becomes more data-intensive.
Addressing these barriers will be essential to unlocking the full potential of digital twins and ensuring clinicians can use this healthcare technology with confidence and consistency. By tackling the challenges early, healthcare systems can create an environment where digital twins strengthen personalised medicine and support safer, more reliable decision-making.
How Clinicians View Digital Twin Adoption
In our latest M3 Pulse, we asked 2581 physicians: How likely are you to adopt digital twin technology (virtual patient models) in your clinical practice in the next 5 years?
The largest group with 30% of physicians, reported they are somewhat likely to adopt digital twin technology in the next five years. Many in this group expressed interest but emphasised that adoption will depend on stronger evidence and practical access within their clinical settings.
Close behind, 29% said they are very likely to adopt the technology, noting clear potential to improve treatment planning, predict complications, and support personalised medicine. These clinicians foresee early but promising applications of the digital twin technology.
Meanwhile, 22% identified themselves as neutral, suggesting they are either unfamiliar with digital twins or unsure about how the technology fits into current clinical workflows. This highlights the need for clearer guidance and educational support around emerging healthcare technology tools.
At the other end of the spectrum, only 3% said they are not at all likely to adopt digital twins, indicating strong resistance is relatively uncommon.
Overall, the findings reflect cautious but growing interest. Clinicians see clear potential in digital twins in healthcare, but wider adoption depends on trustworthy evidence, improved infrastructure, and smoother workflow integration.
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Future Outlook for Digital Twins in Healthcare and Personalised Medicine
The future of digital twins in healthcare points towards higher-precision care and more robust virtual patient models. As data infrastructure improves, digital twin technology may evolve into fully dynamic systems that update continuously, helping clinicians anticipate deterioration earlier and personalise interventions more effectively.
AI-driven simulation tools and predictive analytics in healthcare are expected to strengthen the predictive power of digital twins, enhancing everything from treatment optimisation to pre-operative planning. These advancements align closely with the broader shift towards personalised medicine, where decisions are informed by each patient’s unique biology, lifestyle, and risk profile.
As hospitals continue to invest in advanced healthcare technology, the integration of digital twins is likely to expand across new specialities, enabling safer, faster, and more tailored clinical decisions.
What’s your take on digital twins in healthcare? Share your views in the comments below and tell us where you believe this technology will have the greatest clinical impact.
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