The advocates of machine learning are known to be a fiercely contentious lot, each asserting that its own approach is superior to all others, and that any evidence adduced to the contrary is propaganda, fake news of the worst sort, stemming from jealous advocates of inferior approaches. The closest approximation to the internecine warfare of the machine learning field is the human learning field, in which advocates of public, government-run and union-staffed schools exchange harsh words with advocates of charter schools, with a level of invective and passion that indicates that someone is strongly in favor of hopelessly uneducated machines and/or humans.

The arrival of artificial intelligence and its ilk -- cognitive computing, deep machine learning -- has felt like a vague distant future state for so long that it's tempting to think it's still decades away from practicable implementation at the point of care. And while many use cases today are admittedly still the exception rather than the norm, some examples are emerging to make major healthcare providers take note. Regenstrief Institute and Indiana University School of Informatics and Computing, for instance, recently examined open source algorithms and machine learning tools in public health reporting: The tools bested human reviewers in detecting cancer using pathology reports and did so faster than people. Indeed, more and more leading health systems are looking at ways to harness the power of AI, cognitive computing and machine learning. "Our initial application of deep learning convinced me that these methods have great value to healthcare," said Andy Schuetz, a senior data scientist at Sutter Health's Research Development and Dissemination Group.