How Do Artificial Intelligence, Machine Learning Differ in Healthcare

  • Jul 10th, 2017
  • Guy Paterson

 Hospitals must brace themselves for a complete transformation – a revolution – a total makeover of every aspect of patient care.


If the futurists, visionaries, and venture capitalists are to be believed, artificial intelligence is right on the cusp of becoming the most important breakthrough for healthcare since penicillin. 

new report pins the healthcare artificial intelligence sector at $US7.98 billion dollars in 2022.

While machine learning, semantic analytics, and cognitive computing are advancing at a remarkable clip, true artificial intelligence doesn’t actually exist yet. 

Will it some day?  It’s very likely – and healthcare organizations should start to take the necessary steps that will prepare themselves for a world driven by increasingly advanced machine intelligence.

“There’s a land rush around AI right now...the competitive advantage is going to come from being cognitive."

It may seem like a pedantic semantic argument, but for data scientists and clinical practitioners, the distinction is real and important.  

Machine learning is about recognizing patterns.  With more data and more opportunities to make increasingly granular distinctions based on the successes and failures of the past, a machine learning tool can improve its accuracy iteration after iteration without being told by a human what to do next.

But while machine learning simply serves up results, artificial intelligence must take pattern recognition one step further by planning a future action based on previous results, calculating the probability of that action producing a positive outcome, and executing the action with the highest likelihood of achieving maximum success based on a wide range of constantly changing and often poorly defined parameters. 




Guy Paterson Co-chair - Health Opportunities Committee

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