The Digital Twin: Revolutionizing Healthcare
Digital Twin, Clones, robots that almost look like us.
In science fiction stories, we always imagine the future as a world in which we would share our daily life with robots and other artificial intelligences (AI), having more or less our physical attributes, with the main purpose of serving mankind.
In parallel to these fictions, there is a global apprehension of this cohabitation. Will they take control one day? It is not planned for the moment, but just in case, we put you the famous code of robots enacted by Isaac Asimov's in "Three Laws of Robotics" :
- 1 - A robot may not injure a human being or, through inaction, allow a human being to come to harm.
- 2 - A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
- 3 - A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
- Let's now look at somthing that is more concrete : the concept of digital twin and its impact in health.
PART 1 - Understanding the digital twin in healthcare: the origins of an innovation
Far from futuristic scenarios, let's develop this technological innovation that is supposed to respond to a number of issues.
Without any intention of conquering our world, the digital twin is in fact a concept that has been in use for several years now.
It is defined as a perfect digital copy of -> a subject or a physical object.
Moreover, NASA will use this process in order to make prototype models, to test various launch protocols in particular.
Until then, the advantages are clear:
- on the one hand, the teams save time to carry out all kinds of tests and avoid losing funds,
- on the other hand, terrible accidents are limited.
Dr. Michael Grieves introduced the term Digital Twin in his book "Digital Twin: Manufacturing Excellence through Virtual Factory Replication".
"I introduced the term 'Digital Twin' in (...). However, I attribute it to John Vickers of NASA, with whom I collaborated. We used the term regularly in our various projects."
Seeing the potential of the digital twin, other industries will take notice, including manufacturing, automotive, construction and of course, healthcare.
PART 2 - The digital twin in healthcare: benefits and prospects
A/ Examples of applications of the digital twin in healthcare
Applied to health and medical research, the digital twin manifests itself, for example, by modelling the patient and his body in all its complexity.
Thus, with a view to personalised support, it will be possible to test treatment hypotheses virtually on this digital representation.
In the field of surgery, it is also the assurance of being able to apprehend the risks and other complications.
In pharmacology, it will allow to test the toxicity of certain active ingredients of drugs on a large panel of these digital twins.
In other words, this is one of the keys to advancing medical research.
Indeed, this would allow demonstrations and verification of scientific hypotheses much more quickly and certainly, always in this evolution towards more ethics.
B/ Data structuring and medical improvement
In terms of data structuring, the digital twin is absolutely essential.
Mainly for everything that will be related to machine learning.
We can also imagine how useful it could be for data from health biomarkers.
And we now know that patient data processing is a challenge for medicine.
Let's remember that 80% of patient data is doomed to be lost because of a lack of exploitation. But also due to a lack of time and resources.
With a digital twin, it is the assurance of being able to collect a maximum of targeted data, with the results of the tests carried out, and this, in a structured way.
What's more, the digital twin is part of the deep learning approach.
Deep learning refers to the use of algorithms to identify unresolved situations and understand new cause and effect relationships.
Thus, this technology can use all the knowledge available to it, to raise hypotheses that are still unknown.
However, to do this, AI needs elements to observe in experiments, in particular to apply the comparative method (a database is compared).
With the digital twin, all this is done in a digitalised way
Conclusion: Modelling, simulation, structuring
Today, the digital twin is an integral part of this new era of AI (Artificial Intelligence).
It is revolutionising different sectors.
The use of the digital twin in the medical field is the assurance of a personalised health for the patients, and this, of its management, until the development of new treatments.
Modelling, risk assessment and simulation.
All of these steps help to find new solutions and alternatives.
Researchers can respond to scientific hypotheses by structuring data, thus limiting risks.
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