Today, artificial intelligence (AI) can be found everywhere: in our cars, our smartphones and even our working environments. AI has many areas of application, including in the healthcare sector.

AI will change the interaction between doctors and patients, but most patients won't even know it's involved. That's because improving the patient experience, helping to increase productivity, diagnostic accuracy and overall quality of care won't happen overnight or as part of some massive disruption. The best AI will evolve invisibly with and into the existing care continuum.

Today, hospitals store hundreds of millions of digital images, their numbers growing as imaging scanners such as MRIs and CTs become better at capturing thinner and thinner slices of the body – and 3D and 4D images become the norm. There is simply no way humans can turn that much data into useful information.
AI is vital to tackling the "deluge of data" challenge in healthcare – and medical imaging is a logical place for AI to prove its worth. To do so, man and machine must work together, and radiologists need to appreciate that their roles will transform. By embracing the machine as an integral part of the care team, enabling it to automate routine procedures and processes, clinicians can focus on the most complex and critically ill patients and more efficiently and effectively diagnose and treat disease.

AI-powered medical imaging systems can produce scans that help radiologists identify patterns – and help them treat patients with emergent or serious conditions more quickly. The goal: more accurate, quality care.

The term "artificial intelligence" is used to describe machines or programs that simulate intelligence. This means that these machines or programs are able to understand situations and can therefore help us make decisions — or even make decisions themselves.

Artificial intelligence is built around three components: large data sets and data analysis, scalable computing power, and algorithms that are based on the ability to learn, and one method (among others) known as "deep learning" is in focus.

Machines are most often trained using supervised learning techniques. For example, in the case of medical imaging, the machine will process thousands of images that have already been interpreted. By analyzing this data, the machine can define models that it can then use to interpret some of the elements on its own. For instance, this principle is used in cardiac MRI. Algorithms make it possible to automate some of the more tedious tasks and provide information to improve decision making. This technology therefore leads to faster, earlier and more effective diagnosis.

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