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[+] 209 points 2 years ago (26 children)
  • [–] 128 points 2 years ago* (22 children)

    Using AI for anomaly detection is nothing new though. Haven't read any article about this specific 'discovery' but usually this uses a completely different technique than the AI that comes to mind when people think of AI these days.

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  • [–] 77 points 2 years ago (16 children)

    That's why I hate the term AI. Say it is a predictive llm or a pattern recognition model.

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  • [–] 65 points 2 years ago (6 children)

    Say it is a predictive llm

    According to the paper cited by the article OP posted, there is no LLM in the model. If I read it correctly, the paper says that it uses PyTorch's implementation of ResNet18, a deep convolutional neural network that isn't specifically designed to work on text. So this term would be inaccurate.

    or a pattern recognition model.

    Much better term IMO, especially since it uses a convolutional network. But since the article is a news publication, not a serious academic paper, the author knows the term "AI" gets clicks and positive impressions (which is what their job actually is) and we wouldn't be here talking about it.

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  • [–] 18 points 2 years ago

    It's really difficult to clean those data. Another case was, when they kept the markings on the training data and the result was, those who had cancer, had a doctors signature on it, so the AI could always tell the cancer from the not cancer images, going by the lack of signature. However, these people also get smarter in picking their training data, so it's not impossible to work properly at some point.

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    [–] 121 points 2 years ago (3 children)

    Why do I still have to work my boring job while AI gets to create art and look at boobs?

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  • [–] 103 points 2 years ago* (last edited 2 years ago) (7 children)

    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

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    [–] 83 points 2 years ago (25 children)

    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.

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  • [–] 72 points 2 years ago (11 children)

    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.

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  • [–] 19 points 2 years ago

    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.

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    [–] 59 points 2 years ago (21 children)

    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.

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  • [–] 23 points 2 years ago (8 children)

    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.

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    [–] 45 points 2 years ago (6 children)

    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.

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  • [–] 23 points 2 years ago

    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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    [–] 37 points 2 years ago (37 children)

    Ok, I'll concede. Finally a good use for AI. Fuck cancer.

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    [–] 25 points 2 years ago

    I can do that too, but my rate of success is very low

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  • [–] 25 points 2 years ago (6 children)

    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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    [–] 23 points 2 years ago

    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.

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  • [–] 23 points 2 years ago (7 children)

    pretty sure iterate is the wrong word choice there

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    [–] 22 points 2 years ago (7 children)

    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.

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  • [–] 18 points 2 years ago (1 child)

    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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    [–] 18 points 2 years ago (2 children)
  • [–] 18 points 2 years ago (30 children)

    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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    [–] 17 points 2 years ago* (11 children)

    Yes, this is "how it was supposed to be used for".

    The sentence construction quality these days in in freefall.

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