The Advancement of Healthcare AI

Posted by Healthcare AI on January 29, 2018 with Comments Closed

Healthcare AIArtificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

 

One of the ways Healthcare AI CRISPR. CRISPR has become very popular because of its precision. More so than earlier genetic technologies or healthcare AI, it can accurately target and alter a tiny fragment of genetic code. But it’s still not always as accurate as we’d like it to be. Thoughts on how often this happens vary, but at least some of the time, CRISPR makes changes to DNA it was intended to leave alone. Depending on what those changes are, they could inadvertently result in new health problems, such as cancer.

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Scientists have long been working on ways to fine-tune CRISPR so that less of these unintended effects occur. Microsoft thinks that artificial intelligence might be one way to do it. Working with computer scientists and biologists from research institutions across the U.S., the company has developed a new tool called Elevation that predicts off-target effects when editing genes with the CRISPR. It works like this: If a scientist is planning to alter a specific gene, they enter its name into Elevation. The CRISPR system is made up of two parts, a protein that does the cutting and a synthetic guide RNA designed to match a DNA sequence in the gene they want to edit. Different guides can have different off-target effects depending on how they are used. Elevation will suggest which guide is least likely to result in off-target effects for a particular gene, using machine learning to figure it out. It also provides general feedback on how likely off-target effects are for the gene being targeted. The platform bases its learning both on Microsoft research and publicly available data about how different genetic targets and guides interact.

 

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination. Like so many other new technologies, however, AI has generated lots of unrealistic expectations. We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities. Simply calling a dating site “AI-powered,” for example, doesn’t make it any more effective, but it might help with fundraising. This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption.

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In conclusion, though AI has so many advancements in different industries, healthcare AI still has a ways to go before it can be perfected. Putting too much reliance on it too soon can lead to devastating consequences.