Goto

Collaborating Authors

The Opportunities & Challenges of A.I. In Healthcare - TOPBOTS

#artificialintelligence

When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on healthcare. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. 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 healthcare is to eradicate disease entirely.


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

#artificialintelligence

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

#artificialintelligence

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 in Healthcare Takes Another Step data analytics, technology, readmissions, health plans, population health

#artificialintelligence

Get ready for the next wave of predictive analytics, capable of identifying future admissions and health plan disenrollments. Until recently, many of the machine learning applications talked about for healthcare had been used to teach computing systems enough to be able to suggest a diagnosis on a specific disease. It essentially sent Watson to medical school. IBM had Watson ingest large amounts of medical literature to learn everything physicians are taught about patients' conditions, and then taught it to make diagnoses. But a Harvard professor who leads a startup supplying machine learning technology to Senior Whole Health, a Medicaid managed care organization active in New York state and Massachusetts, says that machine learning will eventually power all technologies we know today as predictive analytics and population health.


Big Data: Healthcare must move beyond the hype

#artificialintelligence

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."