Wound healing is a multifaceted and complex biological process involving various stages including hemostasis, inflammation, and tissue remodeling, leading to the restoration of damaged tissue and skin.¹ In recent years, the evolution of science has seen the emergence of specialized dressings, such as bioactive dressings, which may help to speed the wound healing processes and reduce complications.² Such advancements in medical knowledge and the growing demand for individualized, data-driven approaches are gradually challenging traditional wound care strategies. Consequently, healthcare professionals are incorporating technologies that increase the personalization and precision of wound care to better tailor and monitor treatments. Digital twins are one of these recent technologies that could have great potential in the wound care field. This technology integrates data from patient history, sensors, and medical imaging to develop real-time virtual representations of patients' wounds.³ Digital twin technology enables healthcare providers to monitor healing progress closely, forecast complications, and tailor individualized treatment plans.³

Applying Digital Twin Technology in Wound Care

A digital twin is a computer-generated replica of a patient's wound, which allows for high precision in tracking the healing process. These machine learning models are developed from sensors implanted in wound care products, 3D imaging scans, and the patient's health information. Once a wound's digital twin is generated, it continuously remodels as new data is collected.⁴ For instance, dressing with a sensor may monitor and record temperature, bacterial presence, and moisture levels as imaging captures a clear picture of the wound's status and size. These information updates in the digital twin offer real-time insights into the wound's progress. Simulating the wounds' conditions through different interventions and healing scenarios, digital twins can help healthcare providers make informed decisions and tailor care plans in accordance with the unique characteristics of each wound and an individual response to treatment.³

In real life, digital twins have displayed significant promise. Researchers have examined the impact of digital twin models on chronic wound healing. Sarp et al explored the impact of a digital twin model platform that incorporated wound sensors and imaging to predict wound healing in patients with chronic wounds such as diabetes foot ulcers. This model successfully tailored individualized interventions, leading to fewer complications and a shorter recovery plan as compared with traditional wound care approaches.³ A similar study that examined the integration of technology-based prevention strategies in pressure injury management found that the technology helped detect tissue degradation and infection early, as well as closely monitoring all the healing stages.⁵ The two studies suggest that digital twins are a proactive approach that may allow clinicians to modify treatment promptly, lowering the risk of infection and other complications.^4,5

Perhaps most importantly, digital twins make it possible for healthcare professionals to simulate different scenarios and treatments to determine the most effective intervention for each wound type.⁶ For instance, a model that simulates the effect of different types of wound dressing or use of specific growth factors enables wound care clinicians to make informed, data-driven decisions tailored to individual wound characteristics and needs. Continuous monitoring of a wound is another game-changer. Digital twin models integrate advanced imaging techniques and AI-driven analysis to provide updates on a wound’s depth, size, and tissue composition in real time for improved decision making.⁵ Such data-driven assessments work to help ensure that patients receive evidence-based treatments tailored to their specific conditions. Undoubtedly, personalization is fundamental in digital twins’ technology. No 2 wounds or patients are the same, since factors like general health, environmental conditions, and lifestyle play a role in healing. 

Challenges of Digital Twins in Wound Care

Digital twins require real-time data from sensors, imaging, and medical records to function effectively. Nonetheless, collecting and integrating such data accurately is not any easy task. Inconsistent data collection, different sensor quality, and patient data variations can result in unreliable models, affecting treatment decisions.⁶

Digital twins’ infrastructure is costly to install and maintain, as it requires powerful computers, advanced AI models, and huge data storage. In addition, the cost of software and training for clinicians adds an extra layer of difficulty in digital twins’ wide accessibility. Clinicians also must ensure that there is tight data security when incorporating digital twins while complying with strict regulations of HIPAA.⁷

Digital Twins’ Future in Chronic Wound Management

Digital twins’ future in chronic wound management is promising as technology continues to advance. As artificial intelligence, the Internet of Things, and real-time data tracking advances, digital twins can play a great role in wound care, helping patients heal fast with few complications. Cloud-based platforms will make remote monitoring easier, reducing the strain on hospitals while giving patients high-quality care from home.⁶ Although digital twins face some challenges, ongoing research and advancement in digital health could take this technology a notch higher. With such continued progress, digital twins may have the potential to become a gold-standard tool in wound management. Issues like data accuracy, high installation and maintenance costs, privacy concerns, and system interoperability must be addressed before digital twins can be fully integrated into healthcare.

References

1. Yang F, Bai X, Dai X, Li Y. The biological processes during wound healing. Regen Med. 2021;16(4):373-90. doi: 10.2217/rme-2020-0066

2. Farahani M, Shafiee A. Wound healing: from passive to smart dressings. Adv Healthc Mater. 2021;10(16):e2100477. doi:https://doi.org/10.1002/adhm.202100477

3. Sarp S, Kuzlu M, Zhao Y, Güler Ö. Digital twin in healthcare: a study for chronic wound management. IEEE J Biomed Health Informatics. 2023;27(11):5634-5643. doi:https://doi.org/10.1109/jbhi.2023.3299028

4. Drummond D, Gonsard A. Patient digital twins: an introduction based on a scoping review. medRxiv (Cold Spring Harbor Laboratory). Published February 21, 2024. Accessed March 2025. https://www.medrxiv.org/content/10.1101/2024.02.20.24303096v1 doi:https://doi.org/10.1101/2024.02.20.24303096

5. Ghosh P, Raj P, Vachana MN, et al. Advances in technology-driven strategies for preventing and managing bedsores: A comprehensive review. Arc of Gerontol Geriatr Plus. 2024;1(3):100029. doi: 10.1016/j.aggp.2024.100029

6. Elaziz MEA, Al-qaness MAA, Dahou A, et al. Digital twins in healthcare: applications, technologies, simulations, and future trends. WIREs: Data Mining and Knowledge Discovery. 2024;14(6):1-39. doi:https://doi.org/10.1002/widm.1559

7. Saeed MM, Saeed RA, Ahmed ZE. Data security and privacy in the age of AI and digital twins. In: Digital Twin Technology and AI Implementations in Future-Focused Businesses. Ponnusamy, Sivaram, et al, eds. IGI Global, 2024:99-124. doi:10.4018/979-8-3693-1818-8.ch008