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Artificial Intelligence Comes to the Life Sciences

Posted on September 18th, 2017 by in Pharma R&D


Advances in neural network technology are making it possible to ‘teach’ machines more rapidly—and this machine learning can have big implications for the healthcare and life sciences industry. For instance, artificial intelligence (AI) can be used to analyze health data in order to detect (and then treat) diseases early. AI could also provide critical diagnostic services in remote areas where there aren’t currently enough physicians available to serve patients’ needs.

Given the incredible potential of machine learning, it’s no wonder that in just the last five years, $1.5 billion has been invested in AI start-ups. But even if transferring human expertise to machines and digital platforms are becoming easier, it is still a complicated matter. That expertise is essentially data (often coming from scientific literature) that is being fed to a computer. And, in order to be useful, the data must be reliable, high-quality and relevant. It’s the old “garbage in, garbage out” principle – if you want good results, you have to start with good information.

Check out this article to learn more about the development of machine learning, its impact on life science research and its potential to solve challenges in pharmaceutical R&D.

All opinions shared in this post are the author’s own.

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