this post was submitted on 02 Aug 2024
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Why do I still have to work my boring job while AI gets to create art and look at boobs?
Because life is suffering and machines dream of electric sheeps.
I’ve seen things you people wouldn’t believe.
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Now make mammograms not $500 and not have a 6 month waiting time and make them available for women under 40. Then this'll be a useful breakthrough
It's already this way in most of the world.
Oh for sure. I only meant in the US where MIT is located. But it's already a useful breakthrough for everyone in civilized countries
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Unfortunately AI models like this one often never make it to the clinic. The model could be impressive enough to identify 100% of cases that will develop breast cancer. However if it has a false positive rate of say 5% it’s use may actually create more harm than it intends to prevent.
Another big thing to note, we recently had a different but VERY similar headline about finding typhoid early and was able to point it out more accurately than doctors could.
But when they examined the AI to see what it was doing, it turns out that it was weighing the specs of the machine being used to do the scan... An older machine means the area was likely poorer and therefore more likely to have typhoid. The AI wasn't pointing out if someone had Typhoid it was just telling you if they were in a rich area or not.
That's actually really smart. But that info wasn't given to doctors examining the scan, so it's not a fair comparison. It's a valid diagnostic technique to focus on the particular problems in the local area.
"When you hear hoofbeats, think horses not zebras" (outside of Africa)
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That's why these systems should never be used as the sole decision makers, but instead work as a tool to help the professionals make better decisions.
Keep the human in the loop!
Not at all, in this case.
A false positive of even 50% can mean telling the patient "they are at a higher risk of developing breast cancer and should get screened every 6 months instead of every year for the next 5 years".
Keep in mind that women have about a 12% chance of getting breast cancer at some point in their lives. During the highest risk years its a 2 percent chamce per year, so a machine with a 50% false positive for a 5 year prediction would still only be telling like 15% of women to be screened more often.
Breast imaging already relys on a high false positive rate. False positives are way better than false negatives in this case.
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The most beneficial application of AI like this is to reverse-engineer the neural network to figure out how the AI works. In this way we may discover a new technique or procedure, or we might find out the AI's methods are bullshit. Under no circumstance should we accept a "black box" explanation.
good luck reverse-engineering millions if not billions of seemingly random floating point numbers. It's like visualizing a graph in your mind by reading an array of numbers, except in this case the graph has as many dimensions as the neural network has inputs, which is the number of pixels the input image has.
Under no circumstance should we accept a "black box" explanation.
Go learn at least basic principles of neural networks, because this your sentence alone makes me want to slap you.
Don't worry, researchers will just get an AI to interpret all those floating point numbers and come up with a human-readable explanation! What could go wrong? /s
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iirc it recently turned out that the whole black box thing was actually a bullshit excuse to evade liability, at least for certain kinds of model.
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If it has just as low of a false negative rate as human-read mammograms, I see no issue. Feed it through the AI first before having a human check the positive results only. Save doctors' time when the scan is so clean that even the AI doesn't see anything fishy.
Alternatively, if it has a lower false positive rate, have doctors check the negative results only. If the AI sees something then it's DEFINITELY worth a biopsy. Then have a human doctor check the negative readings just to make sure they don't let anything that's worth looking into go unnoticed.
Either way, as long as it isn't worse than humans in both kinds of failures, it's useful at saving medical resources.
an image recognition model like this is usually tuned specifically to have a very low false negative (well below human, often) in exchange for a high false positive rate (overly cautious about cancer)!
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Ok, I'll concede. Finally a good use for AI. Fuck cancer.
It's got a decent chunk of good uses. It's just that none of those are going to make anyone a huge ton of money, so they don't have a hype cycle attached. I can't wait until the grifters get out and the hype cycle falls away, so we can actually get back to using it for what it's good at and not shoving it indiscriminately into everything.
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And if we weren't a big, broken mess of late stage capitalist hellscape, you or someone you know could have actually benefited from this.
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I can do that too, but my rate of success is very low
This is similar to wat I did for my masters, except it was lung cancer.
Stuff like this is actually relatively easy to do, but the regulations you need to conform to and the testing you have to do first are extremely stringent. We had something that worked for like 95% of cases within a couple months, but it wasn't until almost 2 years later they got to do their first actual trial.
This is a great use of tech. With that said I find that the lines are blurred between "AI" and Machine Learning.
Real Question: Other than the specific tuning of the recognition model, how is this really different from something like Facebook automatically tagging images of you and your friends? Instead of saying "Here's a picture of Billy (maybe) " it's saying, "Here's a picture of some precancerous masses (maybe)".
That tech has been around for a while (at least 15 years). I remember Picasa doing something similar as a desktop program on Windows.
I've been looking at the paper, some things about it:
- the paper and article are from 2021
- the model needs to be able to use optional data from age, family history, etc, but not be reliant on it
- it needs to combine information from multiple views
- it predicts risk for each year in the next 5 years
- it has to produce consistent results with different sensors and diverse patients
- its not the first model to do this, and it is more accurate than previous methods
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Serious question: is there a way to get access to medical imagery as a non-student? I would love to do some machine learning with it myself, as I see lot’s of potential in image analysis in general. 5 years ago I created a model that was able to spot certain types of ships based only on satellite imagery, which were not easily detectable by eye and ignoring the fact that one human cannot scan 15k images in one hour. Similar use case with medical imagery - seeing the things that are not yet detectable by human eyes.
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Yes, this is "how it was supposed to be used for".
The sentence construction quality these days in in freefall.
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