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The Darkness of Big Data - IV Medicine



Big Data and Medicine

Even now servers and their machine learning algorithms are digesting as much medical data they can find.  They have now learned to diagnose medical problems at a truly professional level.  One problem confronting the medical community now is whether to accept such diagnoses as the diagnosis.  This is not a little problem. It is a problem with repercussions across all of medicine, from the school to the courtroom.  Let’s look at a few elementary considerations.

Tools will be put in the hands of the medical practitioner and physician's assistant. The patient may not even qualify to see a doctor until after this “procedure.”

The doctor contradicting the diagnosis is put at legal risk.  Medical research and new procedures will be undermined. On the other hand, if the doctor goes with machine learning, he/she has a legal defense built in. 

The medical schools will teach doctors to rely on the software.  This could undermine their diagnostic discipline, making them the tools of the software.  Sure, they will offer comfort and prescribe the recommend medicine. But their self-confidence will be undermined.  They will relax, losing basic sharpness with their fundamental skills.

The drug companies will become fierce competitors to make their new drugs acceptable for diagnostic recommendations. 

One consequence is that new innovations in medicine will be diminished.  Another is that such engines will strongly enhance the "abilities" poor doctors. (This is actually good.)  The poorest doctors will get better; the better doctors will get poorer, or at best extremely more cautious. 
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This is the fourth part of a series:
http://used-ideas.blogspot.com/2016/04/the-darkness-of-big-data.html
http://used-ideas.blogspot.com/2016/04/the-darkess-of-big-data-ii.html
http://used-ideas.blogspot.com/2016/04/the-darkness-of-big-data-iii.html 
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