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Timeless Quotes - Sadly The Late Paul Shetler - "Its not Your Health Record it's a Government Record Of Your Health Information"


H. L. Mencken - "For every complex problem there is an answer that is clear, simple, and wrong."

Thursday, July 06, 2017

We Need To Keep The Progress In AI And Machine Learning In Perspective. Manage The ‘Hype Cycle’!

This appeared least week

Stanford researchers: Bring expectations for artificial intelligence back down to earth

Jun 29, 2017 8:40am
Healthcare needs to recalibrate its expectations of AI and find ways for the technology to work with physicians.
Artificial intelligence is hitting its stride—at least when it comes to hype.
The unbridled excitement surrounding AI and machine learning technology is higher than ever before, so much so that it’s become a distraction for the medical community, according to two Stanford researchers.
Arguing that AI has reached the “peak of inflated expectations,” Stanford researchers say the healthcare industry needs to shift its focus to how rapidly evolving technology can improve care.
Arguing that AI has reached it’s “peak of inflated expectations,” Jonathan H. Chen, M.D., and Steven M. Asch, M.D., from Stanford University’s Department of Medicine wrote in the New England Journal of Medicine that healthcare can "soften a subsequent crash into a 'trough of disillusionment' by fostering a stronger appreciation of the technology’s capabilities and limitations."
One particularly radical example emerged earlier this month when venture capitalist Vinod Khosla said AI would soon render oncologists obsolete.
“I can’t imagine why a human oncologist would add value, given the amount of data in oncology,” he said during an event in San Francisco hosted by MIT, according to VentureBeat. “They can’t possibly comprehend all of the things that are possible.”
That's the kind of debate that is viscerally satisfying, but ultimately ineffective, the authors contend. Instead, healthcare would benefit from an approach that combines the talents of both humans and algorithms. Healthcare informatics experts took to Twitter on Wednesday night in support of that approach.
AI’s true value will be in decision support, Chen and Asch added. Current AI applications often reach conclusions that are already well known to patients. For AI to make an impact on clinical care, the technology needs to provide predictions that can influence care decisions enough to improve current practice.
More here:
There has been a lot of conversation about this area in the last year of two – especially regarding IBM’s Watson.
It is an area to keep a close eye on while at the same time keeping balanced in expectations.


Bernard Robertson-Dunn said...

"For AI to make an impact on clinical care, the technology needs to provide predictions that can influence care decisions enough to improve current practice."

Such predictions need to be based upon current, complete, accurate data. In other words measure the patient fully, today, now, at this minute. Don't rely on a set of old, stale, dubious, summary pdfs as are found in MyHR.

The most important and only really reliable health data on a patient are those acquired at the time a health professional makes a diagnosis and a decision as to treatment. Then the patient needs to be monitored because every patient is unique and may react differently to the average. Does this describe today's health care environment? Not in my experience.

Should this description form the heart of a Digital Health Strategy? It may not be perfect but it would be a whole lot better objective than life support for a dead duck.

Anonymous said...

Want an example of where technology is making a difference and demonstrates how obsolete the MyHR is:


Now how is the MyHR going to handle what will soon be routinely created data like this, riddle me that Tim Facsimile Kelsey.

Bernard Robertson-Dunn said...

Don't hold your breath. On Tuesday, 15 November 2016 I sat in Tim's office (great view of the harbour bridge) and asked him exactly that question.

I referred to different technology though - have a look at this:

If you go to the site and enter "ehealth" into the search bar you'll see a whole range of development products that people are using to build devices that take simultaneous biometric measurements (15 in the case of the above device).

Let's wait until the strategy comes out and see if the question has been answered.