An introduction to digital twins
Understanding digital twins in healthcare
The role of digital twins in personalized medicine
Benefits of digital twins in medical practice
Challenges and limitations of digital twins in healthcare
The future of digital twins in healthcare
Resources
Further reading
The term “digital twin” was first used in 2003 by Michael Grieves and John Vickers to describe a virtual model that could be used to represent a physical product. Then, the term was used in the field of product engineering.
Today, the concept of the “digital twin” has been adopted by various industries, and the meaning has expanded to encompass digital representations of physical objects, systems, processes, or entities.
Image Credit: Wright Studio/Shutterstock.com
An introduction to digital twins
In recent years, digital twins have become increasingly important in healthcare. In this field, are used to develop digital representations of biological systems, organs, and individual patients. The use of digital twins in this field is revolutionizing how we diagnose, treat, and manage diseases.
By leveraging digital twins in healthcare, scientists are accelerating healthcare innovation, particularly in the areas of medical technology, disease modeling and simulation, patient-centric treatment, surgical planning and training, treatment optimization, and personalized medicine.
Personalized medicine leverages genetic, molecular, and physiological information taken from a patient in order to prescribe treatments that are specific to the individual, thus improving the efficacy of interventions.
This article will focus on the relevance of digital twins to personalized medicine and how it is revolutionizing personalized treatment strategies.
Understanding digital twins in healthcare
Digital twins are used in healthier to produce replicas or simulations of biological systems, organs, and individual patients. Data from various sources is used to create these digital twins: medical imaging, wearable technology, electronic health records, medical tests, and more.
Key aspects of the use of digital twins in healthcare include patient-specific modeling, where digital twins are used to produce models that help healthcare professionals understand the physiology, genetics, and health conditions that are unique to a patient so that their treatment plan can be tailored.
Digital twins are also used in disease modeling and simulation to predict disease progression and clinical outcomes.
This is vital in oncology, where disease modeling can help predict tumor growth in response to treatments. Digital twins are also used to add surgical planning so that surgeons can preview a patient’s anatomy virtually before carrying out a complex procedure.
Applications of Digital Twin to Transform the Healthcare Industry
The role of digital twins in personalized medicine
Digital twins are invaluable to the development of personalized treatment plans. They emerge individual health data to produce a dynamic representation of a person and their disease, which helps healthcare professionals to make informed decisions on how to treat and manage each patient. These decisions are tailored to the individual, rather than one-size-fits-all.
There are many factors that impact how a patient’s disease may respond to treatment. In recent decades, particularly in oncology, we have learned how genetics impacts how different types of cancer are likely to respond to different therapeutic approaches.
With the help of digital twins, experts can tailor treatment plans according to the unique biology of the patient and their disease in order to increase the chances of successful outcomes while minimizing the risk of side effects.
While the concept of digital twins in medicine is still relatively new, already there are numerous examples of how it can be used to enhance personalized medicine. For example, a recent project led by Indiana University used digital twins to develop a self-learning platform for the personalized treatment of melanoma.
So far the project has been successful in developing a multi-scale agent-based model of melanoma pulmonary micrometastases with local and systems-scale immune interactions. While future work is needed to progress the model’s training, this project provides an example of how health data can be used to predict tumor responses to cancer vaccine immunotherapies.
Benefits of digital twins in medical practice
There are a number of benefits to leveraging digital twins in medical practice. Most importantly, the use of digital twins can bring benefits to the treatment and management of diseases (most notably cancer) through the development of personalized medicine.
Perhaps the most obvious benefit of digital twins in this field is the improved accuracy in both disease diagnostics and treatment list offers. This translates to better health outcomes for people with a wide range of diseases.
In addition, the use of digital twins can improve patient safety by reducing the risk of harmful treatment side effects. Finally, by improving the efficacy of treatment, digital twins has the potential to reduce the financial burden of many diseases to healthcare systems around the world.
As well as in medical practice, research studies that utilize digital twins, such as those in the field of cancer research, can be more efficient and result in the improvement of oncology treatment approaches.
Image Credit: MangKangMangMee/Shutterstock.com
Challenges and limitations of digital twins in healthcare
While there are many benefits to the use of digital twins in healthcare, the approach is still developing and must overcome some challenges and limitations. First, the use of digital twins raises ethical concerns, mostly surrounding the ownership of patient data. The widespread use of digital twins would require processes to ensure informed consent, transparency, and ethical practices are upheld.
There are also some challenges regarding resistance to change. The adoption of digital twins might require additional training for healthcare professionals. Embracing new technology is not guaranteed across healthcare systems.
The future of digital twins in healthcare
In the coming years, we will see the use of digital twins in healthcare continue to grow and evolve. Some key trends will likely be the increased integration of artificial intelligence (AI) with digital twins to enhance the efficacy of predictive models. Additionally, we will also likely see the exploration of blockchain technology to enhance the security of data used in digital twins.
Over the next decade, digital twins will likely enhance the capabilities of personalized medicine and may help to improve healthcare outcomes in some diseases.
Resources
- Batty, M. (2018) ‘Digital Twins’, Environment and Planning B: Urban Analytics and City Science, 45(5), pp. 817–820. doi:10.1177/2399808318796416.
- Bruynseels, K., Santoni de Sio, F. and van den Hoven, J. (2018) ‘Digital Twins in health care: Ethical implications of an emerging engineering paradigm’, Frontiers in Genetics, 9. doi:10.3389/fgene.2018.00031.
- Jones, D. et al. (2020) ‘Characterising the Digital Twin: A Systematic Literature Review’, CIRP Journal of Manufacturing Science and Technology, 29, pp. 36–52. doi:10.1016/j.cirpj.2020.02.002.
- Kaul, R. et al. (2022) ‘The role ofaifor developing Digital Twins in Healthcare: The case of cancer care’, WIREs Data Mining and Knowledge Discovery, 13(1). doi:10.1002/widm.1480.
- Stahlberg, E.A. et al. (2022) ‘Exploring approaches for Predictive cancer patient digital twins: Opportunities for collaboration and Innovation’, Frontiers in Digital Health, 4. doi:10.3389/fdgth.2022.1007784.
Further reading