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Use your words! Sorting through the confusing terminology of artificial intelligence | Healthcare IT News

As Leonard D'Avolio, Harvard Medical School professor and CEO of Cyft, a healthcare machine learning company, has noted: 'If I describe what I do as cognitive computing, but a competitor describes what they do as AI or machine learning or data mining, it's hard to even understand what problems we are trying to solve.'

As Steven Astorino VP of development, private cloud platform and z analytics at IBM explained in a blog post, 'Think of machine learning as a set of libraries and an execution engine for running a set of algorithms as part of a model to predict one or more outcomes.

At the HIMSS Big Data and Healthcare Analytics Forum in San Francisco this past year, Zeeshan Syed, Director of the Clinical Inference and Algorithms Program at Stanford Healthcare, offered an explainer of his own for distinguishing between these computer science terms: In an accompanying interview for Healthcare IT News, Syed explained that, at a high level, 'AI is basically getting computers to behave in a smart manner.

For example, if a patient's temperature rises above 102 degrees, the system sends an alert that there's a fever: 'That's getting the computer to behave in an intelligent manner, but it's using existing knowledge embedded in the system.'

With supervised machine learning, the insights derive from both existing data and a specific outcome that might be associated with that data, scientist John Guttag, head of the Data Driven Inference Group at MIT's Computer Science and Artificial Intelligence Laboratory, told Healthcare IT News in 2017.

For example, 'We're given all the people who have Zika infections and then we know which of the women have children with birth defects and which don't – and maybe from that we could build a model saying that if the woman is pregnant and has Zika, what's the probability that her baby has a birth defect,' he explained.

Reinforcement learning, meanwhile, is a specific type of ML that 'typically focuses on being able to sequentially interact and learn from things, and then factor that in to iteratively improve your decision-making over time,' Syed explained.

'With every new emerging discipline there's always a level of confusion around vocabulary, and people having different meanings for the same word,' said Sam Hanna, associate dean of graduate and professional studies and program director in healthcare management at American University.

Indeed, in comments sent to the White House just last week urging continued and conscientious funding for AI research, the American Medical Informatics Association embraced an even different twist on those two omnipresent letters – one that also sought keep the emphasis on carbon-based lifeforms rather than silicon chips.

'In medicine, we tend to frame AI as 'augmented intelligence,' given that there is surely no better example of a scientific discipline so enmeshed with and influenced by the human condition,' said AMIA.