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Comment by jeffreyrogers | Hacker News Reader

I don't think the claim is that AI isn't useful. It's that it's oversold. In any case, I don't think you can tell much about how well your classifier is working for something like cancer diagnoses unless you know how many false negatives you have (and how that compares to how many false negatives a radiologist makes).

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There are two sides to this:

- how good humans are in detecting cancer (hint: not very good) and if having an automated system even as a "second opinion" next to an expert might not be useful?

- there are metrics for capturing true/false positives/negatives one can focus on during learning optimization

From studies you might have noticed that expert radiologists have e.g. F1-score at 0.45 and on average they score 0.39, which sounds really bad. Your system manages to push average to 0.44, which might be worse than the best radiologist out there, but better than an average radiologist [1]. Is this really being oversold? (I am not addressing possible problems with overly optimistic datasets etc. which are real concerns)

[1] https://stanfordmlgroup.github.io/projects/chexnet/

Alright. What is the cost of a false positive in that case?

The problem AI runs into is that with too much faith in the machine, people STOP thinking and believe the machine. Where you might get a .44 detection rate on radiology data alone, that radiologist with a .39 or a doctor can consult alternate streams of information. The AI may still be helpful in reinforcing a decision to continue scrutinizing a set of problem.

AI's as we call them today are better referred to as expert systems. AI carries too much baggage to be thrown around Willy nilly. An expert system may beat out a human at interpreting large unintuitive datasets, but they aren't generally testable, and like it or not, it will remain a tough sell in any situation where lives are on the line.

I'm not saying it isn't worth researching, but AI will continue to fight an uphill battle in terms of public acceptance outside of research or analytics spaces, and overselling or being anything but straightforward about what is going on under the hood will NOT help.

The problem could be fixed by asking doctors to put their diagnosis into the machine before the machine reveals what it thinks. Then, a simple Bayesian calculation could be performed based on the historical performance of that algorithm, all doctors, and that specific doctor, leading to a final number that would be far more accurate. All of the thinking would happen before the device polluted the doctor's cognitve biases.

There is a problem with that approach that at some point hospital management starts rating doctors by how well their diagnoses match those automated ones, and punish those who deviate too much, removing any incentives to be better/different. I wouldn't underestimate this, dysfunctional management exhibits these traits in almost any mature business.

No, it's a "second opinion", and the human doctors are graded with how well their own take differs with the computer's advice, when the computer's advice is different from the ground truth.

And there's probably not even a boolean "ground truth" in complicated bio-medicine problems. Sometimes the right call is neither yes or no, but: this is not like anything I've seen before, I can't give a decision either way, I need further tests.

> The problem AI runs into is that with too much faith in the machine, people STOP thinking and believe the machine.

See https://youtu.be/R_rF4kcqLkI?t=2m51s

In medicine, I want everyone to apply appropriate skepticism to important results, and I don't want to enable lazy radiologists to zone out and press 'Y' all day. I want all the doctors to be maximally mentally engaged. Skepticism of an incorrect radiologist report recently saved my dad from some dangerous, and in his case unnecessary, treatment.

Or for a more mundane example, I tried to identify a particular plant by doing an image based I'd with Google. It was identified as a Broomrape because the pictures only had non-green portions of the plant in question. It was ACTUALLY a member of the thistle family.

Is there a prevailing approach to thinking about (accounting for?) false negatives in ground truth data? I'm new to this area, and the question is relevant for my current work. By definition, you simply don't know anything about false negatives unless you have some estimate of specificity in addition to your labeled data, but can anything be done?

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