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Comment by rscho | Hacker News Reader

Well, yes that's my point. You deprive the docs of most stimuli they really rely on, and call it a comparative study. It's like saying A* is better at finding its way into a maze than a blindfolded man, and call it success.

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>like saying A* is better at finding its way into a maze than a blindfolded man

It's not just better than a blind man. It's better than any human, once you scale the task up beyond a certain point. Consequently, we should not get hung up on the path taken if the ultimate goal is the best possible care for patients in our overloaded public systems. And this paper suggests there is great potential.

Of course there is potential. There is no doubt that computers and robots will someday outperform docs. But to me (an experienced anesthesiologist), this day is probably futher off than most believe. Especially the robot part.

Guess the point of this sub-threat is, that as long as you cannot show it in a well-done study, the potential is just that: potential. But not something tangible.

The big problem is: what does a well-conducted study mean to people? For me personally, I won't be convinced until machines get the same info as the doc, meaning the whole messy file raw plus what can be heard, seen or touched directly, and that's it!

If a machine get's (statistically significant) better results than the docs with less information, that should be good as well, no?

Although I would tend to say we should expect machines to be significantly better than the average doctor. Before we role them out to everyone we should require them to be at least as good as the top 10% of doctors, I guess.

I.e. literally the first sentence if the top post.

I'm not saying doctor's expertise isn't valuable, but studies with even simple linear models have shown that it is much better to feed a doctor's expertise into an expert AI system than to try to teach the AI system to the doctor. And the weighting of the expertise relative to other factors is often lower than you might expect. Even a simple yes/no checklist can be invaluable if it is designed by the top minds of the field.

Clinical scoring (which is what I assume you are referring to as linear models) is mostly useful when the diagnosis is already more or less identified, to estimate prognosis and choose treatment options. They're no exploratory tools. When you 'feed the doc's expertise into a linear system', it means the expertise required to identify the problem in the first place has already done its job.

I don't have the original reference, it was an invited talk at UChicago, but something like https://www.sciencedirect.com/science/article/abs/pii/S09574... for example.

But regarding diagnosis, there are symptom checkers where you feed stuff in. I get the impression that multiple companies are in the process of building such tools based on crawling research papers to diagnose even very rare conditions. IIRC IBM Watson had such functionality.

NHS should be building this for PubMed and offering it for free.

One example: https://ada.com

I mean, it's not like rocket science. More like investing the effort to build a really big decision tree.

Which is how lots of research, including medical/pharmaceutical [1] studies, are done.

[1] Sure, you have to test your new cancer drug on actual cancer patients, but why include the 65 year old obese patient, when you could instead include the 32 year old triathlon athlete into you cohort?

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