A journalist once again nails it as to what AI is not for.

This week a journalist asked me the same question again.
“So AI is good for diagnosing patients better?”
Here we go again.
What a craze journalists have for diagnosis.

They must think that our problem is diagnosis.

Journalists of the world, listen to what we have to tell you today.
Our problem is not diagnosis.
Stop asking about diagnosis.
Our problem is treat-ments.


Sometimes we forget why AI brings a great novelty to medical research.

But it’s not complicated.

It’s very easy.

Listen, I’m going to remind you…

The greatest utility of any clinical data analysis is to find the type of patient who benefits most from an intervention. From a drug, for example.

It’s what you want and what I want. And the patient. And the system. And everyone.

In the past we have been relatively bad at doing this, because even if we tried to come up with a mathematical formula that predicted it, it didn’t contain the variables that actually ended up leading to one outcome or another.

This was so because the variables were picked up by humans.
And we humans have very good things, like humor, compassion, or holidays.

But we have bad things, like cognitive biases and fatigue.

That means we don’t pick up the right variables. Not all of them at least.

We have started to solve this problem since 2013 or so, when we have the capacity to accumulate many more variables because we leave it in the hands of machines.

Which also have the ability to analyze them because they do it by methods that go beyond classical statistics.

If we do an AI project it is possible, not certain, but probable, that we will find new predictor variables that will inform us about the clusters of patients that will respond better.

Sometimes we don’t find the variables but we can rearrange the ones we already knew about into new formulas that in any case predict better.

But that’s enough for today.

If this sounds important to you, we can look at it here.

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