Machine Learning , some thoughts

Bert Verhees bert.verhees at rosa.nl
Mon Jun 25 07:04:51 EDT 2018


On 25-06-18 12:31, Thomas Beale wrote:
>
>
>
> On 25/06/2018 11:21, Stefan Sauermann wrote:
>> 82% of correct recognition rate is a desaster in healthcare.
>
> 92% would be a disaster in healthcare ...
>
>> 74% is even worse.
>>
>> My evidence based feeling is that we still will need to sort it out 
>> manually for some years to come.

So we have always worked with 82% or 92% or 74% recognition, and we 
never called that a disaster. We called that healthcare, and it is only 
a few years ago, that is was like that, and in many cases it is still 
like that.

I now know they are using machine learning for checking x-ray's for 
cancer, and it is a fact that these machine-learning algorithms are much 
better and much more accurate then humans are. The find micro-metastasis 
of only a few cells, and they are of course manually checked. So that is 
a real improvement.

Now, thanks to machine learning the rates of detection are 99%. It is 
not only much better, but also much cheaper. How much do you think it 
costs of a radiologist is staring at pictures?

Thanks for your reply and confirming this.
Bert

>
> I am slightly more optimistic: I suspect that the key bit of research 
> is to create machines, e.g. for interpreting images, that can 
> accurately distinguish between 3 /categories/ of image: don't know; 
> not sure (error rate likely to be too high); and sure (e.g. less than 
> 0.2% error rate or similar). Such machines would throw images in the 
> first two groups to humans, and would do the work on the 'sure' group. 
> The key is to be able to recognise ambiguity or the lack of it.
>
> Doing this properly might require more than one kind of AI. And of 
> course, AI image interpreters would not need to work with displayed 
> bit maps, but would work with computable 3-D and 4-D matrices.
>
> - thomas
>
>
>
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