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Digital twins and artificial intelligence

Digital twins and artificial intelligence

“Real excellence and humility are not incompatible with one another, on the contrary, they are twins.”

Jean-Baptiste Henri Lacordaire, French ecclesiastic, theologian, and journalist

A digital twin was originally designed to be a virtualized representation of a physical object or system. Digital twin is more of a concept rather than a single technology, and entails the use of big data, Internet of things, blockchain, edge computing, cloud computing, and artificial intelligence.

These digital twins are currently being applied, not only to objects and systems, but also as simulation tools for processes, operations, and even experiences and behaviors. With the escalating capabilities of cloud-based data storage and artificial intelligence, these simulations, enabled with digital twins, are reaching new heights.

In healthcare, digital or virtual twins can be used in a myriad of ways:

First, digital twins can be applied to health system operations and processes. This surveillance can be for not only medical devices in order to improve performance but also human processes in different venues (such as operating rooms or emergency departments) to improve dynamics and efficiency. The virtual models can assess various conditions such as manpower shortages, resource depletions, and schedule challenges.

In addition, digital twins can be deployed for population scale situations such as SEIRS (susceptible, exposed, infectious, recovered) models of the COVID-19 pandemic or other population health challenges such as diabetes or mental health. With the advent of real world data for evidence generation for entire populations, virtual simulations can be used to allocate public health resources to maximize impact.

On an individual level, digital twins can be used as in silico modeling for patients with heart failure who can have various simulated models of biventricular pacing and/or ventricular assist devices as well as a new panel of drugs in order to minimize risk and maximize efficacy. This is just one of many examples of how digital twins can be used to improve patient outcomes via precision medicine in the future, with the assistance of genomic sequencing as well as sensors and tests.

As with the other emerging technologies, digital twins as a concept will need to address ethical, legal, and social issues prior to widespread adoption of this powerful capability.

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