How cool would it be to have a digitized "virtual patient" that could tell you exactly whether or not a treatment is working for someone? It sounds a bit sci-fi, but we're moving in that direction right now.
Digital twins are as important to healthcare as sequencing the human genome is to medicine. A Swedish researcher recently wrote this, "Given the importance of the medical problem, the potential of digital twins warrants synergistic research on a scale similar to that of the Human Genome Project."
Digital twin technology is not new. Computer modeling and simulation are common in other fields such as engineering and manufacturing, and the roots of digital twins can be traced back to NASA during the Apollo XIII space program in the 1970s. but digital twins are new to the healthcare industry because the technology is gaining more and more traction in the field, and will continue to grow exponentially in the coming years.
A quarter of healthcare executives in 2021 said their organization is testing the waters of digital twins, in addition to 66% of healthcare executives expecting their investment in digital twins to increase over the next three years.
Digital twins can improve the performance of healthcare organizations, identify areas for improvement, provide customized and personalized medical and diagnostic solutions, and support the development of new drugs and devices.
So what does the digital twin really mean for healthcare?
What does the digital twin mean for personalized medicine?
Digital twins act as a bridge between the physical and digital worlds, using real-time data and data from other sources to support learning, reasoning, and recalibration for improved decision-making. In short, a digital twin is a highly complex digital model that is an accurate twin of a physical thing, and in the case of healthcare, that "thing" is the human body, which is much more complex to model.
Digital twins are used to model an individual's genetic makeup, physical characteristics, and lifestyle to create personalized medicine. As a result, digital twins are more personalized than precision medicine technologies that focus on larger sample sets.
Digitizing the human body to create a fully functional replica of the body's internal systems and organs is intended to enhance medical treatments and patient treatment options. Digital twins in organ modeling can provide many benefits to physicians, such as detecting diseases that have not yet worsened, experimenting with treatments, improving surgical preparation, and more.
Digital twin medical applications are on the rise
In the personalized medicine space, we are seeing digital twins make inroads in virtual organs, genomic medicine, personalized health information, customized medication, full-body scans, and surgical planning.
A great example of this is the Living Heart project, launched in 2014 to use crowdsourcing to create an open source digital twin of the human heart.
The project will lay a unified foundation for computerized cardiovascular medicine that will serve as a common technological foundation for education and training, medical device design, testing, clinical diagnostics, and regulatory science, creating an effective pathway for existing and future cutting-edge innovations to be directly translated into improved patient care.
In genomic medicine, there are Swedish researchers who used network analysis of gene activity in the joints of a mouse model of rheumatoid arthritis to generate a digital twin for predicting the effects of different types and doses of arthritis drugs. Existing rheumatoid arthritis drugs are about 40 to 70 percent ineffective, and the digital twin-based calculations allowed the researchers to screen thousands of drugs that had not yet been tested to predict drugs that might be more effective.
The Empa Research Center in Switzerland is investigating the use of digital twins to optimize drug dosing for patients with chronic pain. Characteristics such as age and lifestyle help customize the digital twin to help predict the effectiveness of pain medications. In addition, patients provide information about the effectiveness of calibrating the accuracy of the digital twin with different doses.
7 Digital Twin Challenges Facing the Healthcare Sector
However, challenges do exist, and here are some of the challenges posed by digital twins in the healthcare industry:
A Bright Future for Digital Twins
Digital Twin Market size is expected to grow from $3.8 billion in 2019 to $35.8 billion in 2025.
Digital twin technology is one of the most important technology trends in healthcare in 2022 and has the potential to help the healthcare industry provide treatment options to patients faster and in a more cost-effective manner.