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AI is ready to take on a massive healthcare challenge

Einat Metzer


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Einat Metzer is CEO and co-founder of Emedgene, a extending precision prescription intellect fellowship.

Which disease outcomes in the highest total fiscal inconvenience per annum? If you guessed diabetes, cancer, coronary thrombosis or even obesity, you suspected wrong. Reaching a mammoth financial headache of $966 billion in 2019, the cost of rare ailments far outpaced diabetes ($ 327 billion ), cancer ($ 174 billion ), coronary thrombosis ($ 214 billion) and other chronic diseases.

Cognitive ability, or cognitive computing solutions, mix neural networks technologies like neural networks, machine learning, and natural language processing, and are able to imitation human knowledge.

It’s not surprising that uncommon diseases didn’t come to mind. By definition, a uncommon canker affects fewer than 200,000 people. Nonetheless, collectively, there are thousands of rare diseases and those affect around 400 million people worldwide. About half of rare sicknes cases are children, and the normal case, young or age-old, condition a diagnostic journey lasting 5 year or more during which they experience endless tests and see numerous professionals before ultimately receiving a diagnosis.

No longer a moonshot challenge

Shortening that diagnostic odyssey and reduce the number of affiliated penalties was, until very recently, a moonshot challenge, but is now within reach. About 80% of uncommon infections are genetic, and technological sciences and AI advancements are mixing to procreate genetic tests widely accessible.

Whole-genome sequencing, an advanced genetic research that allows us to examine the entire human DNA , now expenditure under $1,000, and market master Illumina is targeting a $100 genome in the near future.

The remaining challenge is interpreting that data in the context of human health, which is not a insignificant challenge. The typical human contains 5 million unique genetic variants and of those we need to identify a single disease-causing variance. Recent some progress in cognitive AI allow us to interrogate a person’s whole genome sequence and identify disease-causing mechanisms automatically, augmenting human capacity.

A switch from narrow-minded to cognitive AI

The path to a universally usable AI solution necessitated a paradigm switching from shrink to broader machine learning examples. Scientists interpreting genomic data review thousands of data points, collected from different sources, in different formats.

An analysis of a human genome can take as long as eight hours, and there are only a few thousand modified scientists worldwide. When we are to achieve the $100 genome, commentators are expecting 50 million-60 million people will have their DNA sequenced every year. How will we analyze the data generated in the context of their own health? That’s where cognitive knowledge comes in.

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