With the accelerating pace of AI and technology, we keep asking ourselves, “What is the next frontier of technology and how will it advance humanity?” One answer: creating a digital twin for each of us.
What is a digital twin? In simple terms, a digital twin is a virtual or digital replica of a living organism or a part of that organism such as a heart or a lungs. A digital twin is created from data points such as image recordings, personal measurements, laboratory results, and genetics. All of this data is then used to create an exact digital replica of a person.
Why do we need this? To prevent possible excess 250,000 deaths per year. So many people die from medical errors every year, according to Johns Hopkins.
The human digital twin
The concept itself is easy to understand, a virtual representation of a physical object over its life cycle. However, the technology and its uses in healthcare are potentially endless and revolutionary.
In a recent conversation with Dr. Marko Grobelnik, AI researcher and digital champion Slovenia as well as technical director of the International Research Center for AI at the Jožef Stefan Institute, he made it clear why the digital twin technology will be a decisive step in the development of the health care system. He noted that healthcare facilities are complex systems with many nested processes that only deliver results when properly synchronized. Each of the processes is associated with a certain degree of uncertainty, which can either be harmful or cause an undesirable domino effect with potentially catastrophic consequences. To navigate such a complex system through a series of known and unknown obstacles, digital twin technology can be helpful because it works with the breadth of observation and speed of reaction that humans do not have.
His assessment is spot on.
With the full development of digital twins, we could exponentially speed up work, save time, but above all life.
Practical use of a digital twin
Once a digital twin is created, its healthcare use is intended to be the test object for treating disease or injury before it is used on the person or people in general. Then, with enough biological data, we can carry out more precise and effective medical interventions that are tailored to the individual patient. This is becoming more and more possible due to rapid advances in computing power and the ability to process extremely large amounts of data.
With sufficient knowledge of the human genome, individualized genome sequencing could be performed for a person who may have cancerous tumors. The behavior and reaction of these tumors could be digitally analyzed and examined by an AI system, and then an individualized biological agent could be generated to fight the tumors. These remedies offer three advantages over traditional treatment:
- They have been specially developed for the individual and their unique cancer variant, so that the chances of success are significantly improved.
- They are made up of the patient’s DNA, so the body will not reject them.
- The funds do not cause severe side effects from chemotherapy or radiation.
After all, these types of active ingredients should either be injectable or even available in pill form.
Because digital twins are digital models, they can be analyzed to provide the patient with options to prevent future diseases, provide instructions for preventive maintenance, and even offer performance improvements. Not just a cure, but a health and personal care guide to avoid and prevent future illnesses.
The reason this is not our current reality is in large part because of the extreme complexity of the human genome and the seemingly myriad of ways it could respond to disease and medicine. Despite this current roadblock, projects are active that have potential.
For starters, the British government initiated the 100,000 Genome Project Sequence entire genomes of 85,000 National Health Service patients. The project focuses on common cancers and infectious diseases. In the US, studies like that Human Longevity Inc.. and the Mayo Clinic Center for Individualized Medicine Gather genomic information on large numbers of people to provide more data for scientists and technologists.
Current practical use cases
Although the fully digital twin described so far is still in the future, there are current practical applications that prove that we are on the right track. The first of these is patient monitoring. Wearables are something that many of us live with today. Whether it’s a Fitbit or some other health and fitness tracker, as a society we have gotten used to this technology and accepted it as ubiquitous. The same technology can be used to feed real-time data to a digital twin in the cloud that develops models that detect disease symptoms early on.
Second is the operation simulation and risk assessment. A real-life example is the France-based startup Sim & Cure, which has developed a Patient-based digital twin for treating aneurysms. Sim & Cure uses simulations and digital twins to help neurosurgeons maximize patient safety during treatment. It is not only practiced on a simulated patient, but it is practiced precisely on the patient.
Third is diagnosis and treatment decision support. Using data from various health sources such as imaging records, personal measurements, laboratories, and genetics, healthcare professionals get a complete and real-time picture of a patient and their previous and current health status. The digital twin then simulates the patient’s state of health and the AI technology fills any gaps with the most accurate, relevant, and scientifically sound information available.
An example is the start, Babylon health that’s what the Healthcheck app developed. After users complete a lifestyle and family history questionnaire, the AI-powered app creates a digital twin that provides users with insights into their current health and risk factors for future conditions, as well as practical recommendations for healthy living.
Fourth, it is not the patient but the hospital itself. An NHS trust in Manchester, UK, has worked on a partnership with Hitachi Ltd. to digitize its processes and optimize its human resources. It will also create a “digital twin” of hospital operations for clinicians and managers to model possible changes in how care is organized. The aim is therefore to make all hospital work more intelligent and to put the nursing staff in better positions in order to guarantee the best possible care.
The future of digital dual health technology
The technology to create digital twins in healthcare is in place. The challenge for the healthcare industry is to test this technology and apply it to specific problems.
A road map? Scientists at Linköping University in Sweden created digital twins of mice with rheumatoid arthritis by sequencing their RNA into digital models. Computer simulations were then run to determine which drugs were most effective in treating individual mice.
We don’t have to wait any longer and scale this technology to people.
If implemented successfully, we can say goodbye to human clinical trials forever. We can test all possible vaccines and treatments on digital twins, save lives faster, and never test potentially dangerous treatments on humans again. This is the future digital twin technology can create. And that’s the future we need.
Tsvi Gal, director of enterprise technology at Memorial Sloan Kettering Cancer Center and former executive director of Morgan Stanley, contributed.
Mark Minevich is President of Going Global Ventures. He is a global digital cognitive strategist, artificial intelligence expert, and venture capitalist. He also serves as Executive Chair, Digital Pioneers Network and Chief Digital Strategist at the International Research Center for AI under the auspices of UNESCO. He is a member of the B20 Digital Economy Task Force in the G20 Presidency, a member of the World Economic Forum Council on AI for Humanity, Senior Fellow of the US Council on Competitiveness, Advisor to the Boston Consulting Group and Digital Fellow at IPsoft / Amelia. Follow him on Twitter @Minevich.