Digital Twin: Doubling The Power Of Health Diagnosis

If given a choice, would you like your twin? It indeed is fun to confuse people with two of the same kind! 

Jokes apart, if you really want a twin, you should probably consider a digital twin (no, not to play pranks). They are the new tools to ensure your wellness.

Can you do that? Well, technically, yes! 

But not in the form you are probably expecting. As technology stands today, a complete and accurate replica of a human being is a little further in the future.   

Let’s understand the basics.

What is a digital twin?

To put it simply, digital twins are digital representations of physical objects. Digital twins are highly complex models that use artificial intelligence (AI) and large amounts of digital and physical data to mimic a real-world object accurately. The object could be a process (a production line, for example), a person, a device, or a system. Digital twins are built to represent and understand regions and cities also.

Creating a digital twin begins with the mapping of a physical object or space through imagery and LiDAR–down to every minute detail. After a digital replica is created, real-world data is added to the digital twin. The two are continuously synchronized, ensuring the twin always has the most current information. Computer vision, AI, and machine learning (ML) process the information, allowing users to model possible scenarios and outcomes on the twin’s real-world counterpart.


Image Courtesy: ASME

How does this help in healthcare?

Imagine being able to predict the impact of experimental treatments on a patient without risking their life. How about generating a digital representation of a person’s body–down to the cellular level–to choose the best surgical option? What if we could determine the odds that a pacemaker will keep a patient with congestive heart failure alive without the need for surgery?

These important and relevant questions hinge on the life and death situation of a patient. 

What is the answer? Digital twin, of course!

How can one use digital twins?

Though the use of digital twins in healthcare is still in a relatively nascent stage, there have been some remarkable breakthroughs.

These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations, as well as drug and device manufacturers. 

Image Courtesy: Verywell Health

Digital twins show tremendous promise in making it easier to customize medical treatments to individuals based on their unique genetic makeup, anatomy, behavior, and other factors. 

They can also model hospital environments, operating rooms, and medical instruments.

Applications of Digital Twins

The potential applications range across the spectrum of healthcare services.

Virtual organs

Several companies are working on modeling virtual hearts customized to individual patients, updated to understand the progression of diseases over time and evaluate the response to new drugs, treatments, or surgical interventions.

Genomic Medicine

Swedish researchers have mapped mice RNA into a digital twin to predict the effect of different types and doses of arthritis drugs. The goal is to personalize human diagnosis and treatment using RNA. The researchers observed that medication does not work about 40% to 70% of the time. Experiments conducted to map the characteristics of human T-cells play a crucial role in understanding immune defense. These maps can diagnose common diseases sooner for cheaper and more effective treatments.

Customized drug treatment

The Empa research center in Switzerland works on digital twins to optimize drug dosage for people affected by chronic pain. Characteristics such as age and lifestyle help customize the digital twin that predicts the effects of pain medications. In addition, patient reports on the results of different dosages calibrate digital twin accuracy.

Medical device development

Regulators are exploring the use of digital twins for modeling personalized medical devices that include advanced manufacturing of personalized prosthetics, assistive technology, and other equipment. 

Disease modeling

Digital twins are being used to study diseases such as Alzheimer’s and multiple sclerosis to better understand treatment options and accelerate trial timelines. 

Digital therapeutics and virtual reality-based therapies

With clinical evidence and real-world data, digital twins can create simulations of new treatments and bring life-saving innovations to market more quickly. As software-based treatments gain FDA clearance, such as virtual reality therapies and prescription digital therapeutics, digital twins could also help prove these products will perform well outside a clinical trial setting.

Hospital operations

Digital twins can be used to replicate staffing systems, capacity planning, workflows, and care delivery models to improve efficiency, optimize costs, and anticipate future needs.

Facing the hurdles

The challenges to implementing digital twins in clinical practice can be categorized into three areas:
– computational requirements,
– clinical implementation,
– data governance and product oversight

The progress of AI-driven personalized medicine is contingent on data fusion–the integration of big data from several data sources containing heterogeneous information. Many of these sources, such as EHR (Electronic Health Record) data and imaging reports, present clear obstacles to efficient data coding and sharing. Digital twins need to integrate data pre-processing techniques like natural language processing to overcome these integration challenges. Additionally, data fusion methods should account for the varying computational complexity of real-time patient data.

Amid ongoing frustrations with existing technologies like EHR and common concerns of bias in AI models, transparency and education regarding digital twins are key to garnering trust from patients on this new-age technology. Without the patient’s approval and consent, clinics are restrained from using digital twins to their maximum potential.

Given the complexity of these technologies, regulators (like the FDA) should use advisory committees, including expert opinions from data scientists on the models and datasets. Regulators must also enact policies to protect patient rights, including informed consent and privacy, in this rapidly evolving innovation landscape. Given the highly detailed personal health data represented in digital twins, the digital twin data economy presents new threats to data privacy.

The evolution of digital twin technology is unstoppable. Despite the challenges, the advancements in trusted AI, and the proliferation of digital-twins-as-a-service, digital twin technology is set to grow at a compounded rate of 39% from 2023 to 2030.

Ready to meet your twin?