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3 Use Cases & Benefits of Digital Twins in Healthcare for 2024

Written by
Hazal Şimşek
Hazal Şimşek
Hazal Şimşek
Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation.

She has experience as a quantitative market researcher and data analyst in the fintech industry.

Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.
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3 Use Cases & Benefits of Digital Twins in Healthcare for 20243 Use Cases & Benefits of Digital Twins in Healthcare for 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

It’s been reported that 66% of healthcare executives expect increasing investment in digital twins over the next three years. This is because digital twins improve healthcare organization performance, discover areas for improvements, provide customization and personalization of medicine and diagnosis, and enable the development of new medicines and devices.

In this article, we explore digital twin benefits, use cases and challenges in healthcare.

How is digital twin technology used in healthcare?

A digital twin is a digital replica of the tools, people, processes, and systems that businesses employ. In healthcare systems, digital twins are utilized to build digital representations of healthcare data, such as hospital environments, lab results, human physiology, etc. through computer models. To construct virtual twins, data that covers the individual, population traits, and environment are used. 

Digital twin of a healthcare facility

Digital twin technology can be used to generate a virtual twin of a hospital to review operational strategies, capacities, staffing, and care models to identify areas of improvement, predict future challenges, and optimize organizational strategies. Therefore, digital twins of hospitals can be used for generating facility replicas, and in turn this enables:

  • Resource optimization: Leveraging historical and real-time data of hospital operations and surrounding environment (e.g. COVID-19 cases, car crashes, etc.) to create digital twins enables hospital management to detect bed shortages, optimize staff schedules, and help operate rooms. Such information increases the efficiency of resources and optimized the hospital’s and staff’s performance, while decreasing costs. For example, a review study has shown that utilizing digital twins to manage the smooth coordination of several processes enabled a hospital to reduce the time in treatment of stroke patients by.
  • Risk management: Digital twins provide a safe environment to test the changes in system performance (staff numbers, operation room vacancies, device maintenance, etc.) which enables implementing data-driven strategic decisions in a complex and sensitive environment.

Digital twin of the human body

Digital twins are also applied for modeling organs and single cells or an individual’s genetic makeup, physiological characteristics, and lifestyle habits to create personalized medicine and treatment plans. These replicas of the human body’s internal systems improve medical care and patient treatment by:

  • Personalized diagnosis:
    • Digital twins allow collection and usage of vital data (e.g. blood pressure, oxygen levels, etc.) at the individual level which helps individuals to track persistent conditions and, consequently, their priorities and interactions with doctors by providing basic information. Thus, such personalized data serve as the basis of clinical trials and research data at labs.
    • By focusing on each individual separately, doctors do not derive treatments from large samples. Rather, they rely on customized simulations to track the reactions of each patient to different treatments, which increases the accuracy of the overall treatment plan. Despite the interest and increasing amount of efforts for personalized medicine, there are no digital twins applications for actual patients. One of the centers specialized on personalized medicine is Linköping University in Sweden who mapped mice RNA into a digital twin to predict the effects of medication.
  • Treatment Planning: With advanced modeling of the human body, doctors discover the pathology before the disorders are evident, experiment with treatments, and improve preparation for surgeries.
Two doctors are viewing a replica of the human body with 3-D virtual reality glasses as an example of digital twin in healthcare.
Figure 1: Digital twin of human body

Digital twins for medicine and device development

Digital twin in healthcare can improve the design, development, testing, and monitoring of new drugs and medical devices. For example:

  • Drugs: Digital twins of drugs and chemical substances enable scientists to modify or redesign drugs considering particle size and composition characteristics to improve delivery efficiency.
  • Devices: A digital twins of a medical device enables developers to test the characteristics or uses of a device, make alterations in design or materials, and test the success or failure of the modifications in a virtual environment before manufacturing. This significantly reduces the costs of failures, and enhances the performance and safety of the final product.
Two researchers are testing a new medication through digital twins, another use case for digital twin in healthcare.
Figure 2: Digital twin for medication

What are the digital twin challenges in healthcare?

Some of the challenges that face digital twin implementation in healthcare include:

Limited adoption

Digital twin technology is not widely adopted in the clinical routine. Healthcare units (e.g., hospitals and labs) should increase the impact of technology on digital simulations, vital clinical processes, and overall improvement of medical care.

On the other hand, even though healthcare system uses digital twins increase, it is argued that it will remain expensive and not accessible for everyone. Digital twin technology will become a benefit reserved for people with higher financial capabilities, which would generate inequality in healthcare system.

Data quality

Artificial intelligence system in digital twins learn from the available biomedical data but as the data is gathered through private companies, the data quality might turn out bad. Consequently, the analysis and representation of such data becomes problematic. That eventually affects the models negatively, which also affects the reliability of the models in the diagnosis and treatment processes.

Check our article on data-centric AI to learn more about how you can improve the quality of your data in AI systems.

Data privacy

The applications of digital twins require gathering more and more individual level data by healthcare organizations and insurance companies. Over time, these health organizations grasp a detailed portrait of a biological, genetic, physical, and lifestyle related information of a person. Such personalized data might be in use benefitting the company’s interest instead of the individuals. One example would be that insurance company might leverage the data to increase precise distinctions significant to personal identity.

Feel free to explore data security best practices.

Further reading

To learn more about digital twin technology and discover its use cases and applications in other industries, you can read our in-depth articles:

If you believe your business will benefit from a digital twin, feel free to check our data-driven list of digital twin software.

And let us help you choose the right tool for your business:

Find the Right Vendors
Hazal Şimşek
Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation. She has experience as a quantitative market researcher and data analyst in the fintech industry. Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.

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1 Comments
Yuji Suzuki
Jun 09, 2022 at 15:10

Thank you for this very comprehensive article.
I am a fellow for Health Education England, and I am wondering whether you could share some more insight to our current training doctors.
Looking forward to hearing from you.

Warmest Wishes

Yuji Suzuki

Cem Dilmegani
Nov 18, 2022 at 07:29

Of course, reached you via email,

Thank you

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