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The opportunities and challenges of AI in health care VentureBeat AI

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When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on health care. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with AI we could achieve exponential breakthroughs. Deep learning first caught the media's attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford's School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists. The ultimate dream in health care is to eradicate disease entirely.


Data analytics aids value-based reimbursement, but bigger goals loom

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Healthcare organizations have spent years installing electronic health records and other information systems that collect data and improve patient care. Is the information collected by Fitbits and Apple Watches covered by HIPAA regulations? Find out more about what's covered – and what isn't – when it comes to wearable devices and data so you can avoid the risks. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.


The mashup approach: How healthcare can save billions on AI and machine learning

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Healthcare is at a two-tined fork: One strip leads to repeating the same mistakes others have already made while the more enlightened rail learns from those instead.


Machine learning: Changing everything but healthcare

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Machine learning has proven it can beat traditional human techniques in healthcare for some time now, yet it remains limited in use in the healthcare industry. But that may be about to change. "Machine learning is changing everything -- except maybe healthcare," MIT professor John Guttag said here at the Big Data and Healthcare Analytics Forum on Oct. 24. While machine learning drives products and services such as Google Maps, many websites' tracking of shopping habits and presenting options, banking, credit card companies and others, healthcare providers have done much less with the existing technologies. "There's lots of talk, but very little action, very little progress in healthcare," Guttag said.


Big Data: Healthcare must move beyond the hype

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The hype surrounding so-called Big Data – the computational analysis of vast data sets to uncover patterns, trends and associations – is "bi-polar." That's how Leonard D'Avolio, an assistant professor at Harvard Medical School, describes all the chatter around this technology. "Either we are reading about how Big Data will cure cancer or about how it's foolish to believe Big Data will replace doctors," D'Avolio said. "I think the narrative should be in the middle, where we are talking about these technologies as tools that could be used to complement the work of not just clinicians but also healthcare administrators, operational leaders and others. Big Data is another set of technologies with pros and cons."