Algorithms, datasets, machine learning, deep learning, cognitive computing, big data, and artificial intelligence: IT expressions that took over the language of 21st-century healthcare with surprising force. If medical professionals want to get ahead of the curve, they rather get familiarized with the basics of A.I. and have an idea of what medical problems they aim to solve. So, let’s take a closer look at machine learning and deep learning in medicine.
The term “artificial intelligence” might be misleading as due to the overuse of the expression, its meaning started to get inflated. It implies software with cognition and sentience, a far far more developed technology than it stands at the moment. For example, Facebook announced an A.I. to detect suicidal thoughts posted to its platform, but closer inspection revealed that the “A.I. detection” in question is little more than a pattern-matching filter that flags posts for human community managers.
At best, current technology with various algorithmic methods is able to reach artificial narrow intelligence (ANI) in some fields, the most advanced areas being computer vision and natural language processing. In very simple terms, ANI has incredible pattern recognizing abilities in huge data sets, which makes it perfect for solving text, voice, or image-based classification and clustering problems. But while more complex data analysing methods sound exciting and appealing, sometimes you can arrive at great results by using less advanced techniques, too.
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