Medicine's Machine Learning Problem
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Medicine's Machine Learning Problem
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Re: Medicine's Machine Learning Problem
#2Non clickbait headline: Medicine has some data collection biases (if your aim is to represent US demographics).
Long existing non-ML methods suffer due to this data collection bias but again for some reason the author seems to put AI in a special mysterious place on a pedestal. They use innuendo and anecdote to make assertions without evidence to back them up as systemic problems unique to AI. Innovation will never happen if new technology has to perform perfectly, it only has to perform better than existing methods.
Re: Medicine's Machine Learning Problem
#3It's a hard problem to work around which is rooted in the data available. I published this paper while I was at Google:
https://www.nature.com/articles/s41591-019-0447-x
The only data we were able to get at the time was mostly white patients. We talked to many hospitals but many were/are reluctant to share anonymized data for research. I'm not at Google so I'm not sure the status of the project now, but there was a real attempt to try and gather more diverse data. Unfortunately there were a lot of obstacles put up by those who have the data (hospital systems).
Fundamentally, it seems to me like there just aren't as many lung cancer screening scans out there for non-white patients as there are for white patients. How do we get around this? How do we improve on the situation? I fundamentally believe that machine learning in the long term can make medicine more accessible to more diverse groups, but not if we shoot it down out of fearmongering right away.
I agree that bias is a problem, but part of what needs to happen to get more diverse data is simply having more data available. There is real promise in this technology and if we have a one dimensional view of it ("is it or is it not dangerous because of bias/privacy") then we will fail to get past the initial humps related to fear and distrust.
Re: Medicine's Machine Learning Problem
#4Non clickbait headline: Medicine has some data collection biases (if your aim is to represent US demographics). Long existing non-ML methods suffer due to this data collection bias but again for some reason the author seems to put AI in a special mysterious place on a pedestal. They use innuendo and anecdote to make assertions without evidence to back them up as systemic problems unique to AI. Innovation will never h…
I'm of two minds about this article. It does a reasonable job of enumerating the issues with naively deploying ML in a healthcare setting. However, these articles are becoming a dime a dozen and there is little actionable talk on how to discover or mitigate these issues at a level that practitioners can use.
To your point about the bar for new tech, I agree that singling out AI/ML is a cheap shot and more speculative FUD without concrete evidence. That said, we have seen no shortage of hucksters and self-aggrandizing members of the "move fast and break things crowd" trying to treat medicine as a beginner-level Kaggle challenge. This has become particularly egregious and noticeable during the pandemic [1]. The respective lack of medical and technical literacy among programmers/"data scientists" and healthcare providers/admins is just more fuel for the fire.
[1] https://www.reddit.com/r/MachineLearning/comments/fni5ow/d_w...
Re: Medicine's Machine Learning Problem
#5Question about this: they talk about how datasets are not representative, my question is, compared to what?
I’m guessing by the politic of this article the person is from the US, do they want people to look at data representative of US population? That seems pretty narrow-minded.
My country has almost no black people (probably Perhaps they are advocating we use world demographics but that would be logistically impossible for basically all researchers around the world.
Am I misunderstanding the author or does the article seem pretty ethnocentric? (is that the right word?) US-centric?
Wouldn’t it be better to just qualify/label the demographics of the research data (we used all black people, all white people etc). They talk as if there is some golden ratio we should all be following but that just isn’t the case.
In any case, I don’t think it is useful to shame researchers that are just doing the best they can with the data available cause the data they have is useful to someone.
Re: Medicine's Machine Learning Problem
#6One problem is mistaking statistical analysis for learning, data for knowledge
Re: Medicine's Machine Learning Problem
#7Question about this: they talk about how datasets are not representative, my question is, compared to what? I’m guessing by the politic of this article the person is from the US, do they want people to look at data representative of US population? That seems pretty narrow-minded. My country has almost no black people (probably Perhaps they are advocating we use world demographics but that would be logistically imposs…
For better or worse, most of these articles are US-centric because that's where most of the R&D money for health ML is.
The far more reasonable approach is to make sure your data contains as many demographics as possible (not just race) from your actual patient population. If there happen to be gaps, then put in at least a reasonable effort to fill them instead of shrugging and saying "it's out of our control". That, along with a per-demographic breakdown of important metrics and your point about qualifying demographics in the data (which is already done in most medical publications, including many using ML) would already be a huge improvement on what most people do now.
Ironically, it's the big tech companies that have the hardest time with this because they want to make generally deployable projects, yet don't have access to as much data as many healthcare orgs do. Frankly, I don't have much sympathy for them: a lot of this is trust issues from self-inflicted damage.
Re: Medicine's Machine Learning Problem
#8Question about this: they talk about how datasets are not representative, my question is, compared to what? I’m guessing by the politic of this article the person is from the US, do they want people to look at data representative of US population? That seems pretty narrow-minded. My country has almost no black people (probably Perhaps they are advocating we use world demographics but that would be logistically imposs…
For better or worse, most of these articles are US-centric because that's where most of the R&D money for health ML is. The far more reasonable approach is to make sure your data contains as many demographics as possible (not just race) from your actual patient population. If there happen to be gaps, then put in at least a reasonable effort to fill them instead of shrugging and saying "it's out of our control". That,…
> make sure your data contains as many demographics as possible (not just race) from your actual patient population
By this you mean researchers should mirror their patient population as closely as possible (be it socioeconomic, gender, race etc) in whatever region they may operate in (which may not scale perfectly worldwide but will serve patients in that region well)?
Re: Medicine's Machine Learning Problem
#9It's a hard problem to work around which is rooted in the data available. I published this paper while I was at Google: https://www.nature.com/articles/s41591-019-0447-x The only data we were able to get at the time was mostly white patients. We talked to many hospitals but many were/are reluctant to share anonymized data for research. I'm not at Google so I'm not sure the status of the project now, but there was a r…
As a fellow practitioner, I entirely agree. Actually, reading this article made something click for me regarding the oft discussed and denigrated “bias in AI” always brought up in discussions of the “ethics of AI”: there is no bias problem in the algorithms of AI.
AI algorithms _need_ bias to work. This is the bias-variance trade off: https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff
The problem is having the _correct_ bias. If there are physiological differences in a disease between men and women and you have a good dataset, the bias in that dataset is the bias of “people with this disease”. If there is no such well-balanced dataset, what is being revealed is a pre-existing harmful bias in the medicinal field of sample bias in studies.
If anything, we should be thankful that the algorithms used in AI, based on statistical theory that has carefully been developed over decades to be objective, is revealing these problems in the datasets we have been using to frame our understanding of real issues.
Next up, the hard part: eliminating our dataset biases and letting statistical learning theory and friends do what they have been designed to do and can do well.
Re: Medicine's Machine Learning Problem
#10One problem is mistaking statistical analysis for learning, data for knowledge
It's only a problem if it doesn't work.
I'm happy with imperfect protein folders that beat SOTA by 100% and with DALL.E drawing the radish-in-tutu-walking-a-dog and a harp-snail on request. I'll be happy also with the slightly unexplainable medical diagnosis that still beats the average expert in that field. And getting good unbiased data for these algorithms is going to happen eventually.