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Artificial Intelligence: Enabler for Progress in Healthcare

Clinics, administration, and management benefit from smart algorithms

Artificial intelligence (AI) is hardly missing from any event agenda in the context of healthcare. Young companies that mention AI in their pitches have excellent prospects of attracting investor interest. What is behind AI in medicine? Does the approach deliver on its value proposition?
For decades, artificial intelligence was a goal pursued by researchers and developers in many business sectors. While real-world benefits were scarce during that time, the situation has changed significantly in recent years, with more powerful computers, readily available data, and the combined efforts of computer scientists, physicians, and hospital managers now enabling the technology to deliver on more and more of its value propositions.

Levels of Artificial Intelligence

Though the term is interpreted in different ways, a threefold division is plausible: Pattern recognition, machine learning, and neural networks (sometimes called deep learning).Pattern recognition is successfully in use particularly in medical imaging, with some clinical studies rating the AI recognition as good as that of a human.

In machine learning, an algorithm learns to perform a task independently through repetition. In doing so, it is guided by predefined quality criteria and the information content of the data. Unlike conventional algorithms, machine learning does not model a solution path.

As the name suggests, neural networks are inspired by nerve cell connections in the human brain. The artificial analog of neurons and synapses consists of multiple rows of data nodes that are interconnected with weighted links. Learning algorithms in neural networks may be continuously trained with new data. Deep neural networks can have an enormous number of neuron layers; thus, Deep Learning can be used to solve complex problems.

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