My knowledge on this is several years old, but back then, there were some types of medical imaging where AI consistently outperformed all humans at diagnosis. They used existing data to give both humans and AI the same images and asked them to make a diagnosis, already knowing the correct answer. Sometimes, even when humans reviewed the image after knowing the answer, they couldn’t figure out why the AI was right. It would be hard to imagine that AI has gotten worse in the following years.
When it comes to my health, I simply want the best outcomes possible, so whatever method gets the best outcomes, I want to use that method. If humans are better than AI, then I want humans. If AI is better, then I want AI. I think this sentiment will not be uncommon, but I’m not going to sacrifice my health so that somebody else can keep their job. There’s a lot of other things that I would sacrifice, but not my health.
iirc the reason it isn’t used still is because even with it being trained by highly skilled professionals, it had some pretty bad biases with race and gender, and was only as accurate as it was with white, male patients.
Plus the publicly released results were fairly cherry picked for their quality.
Medical sciences in general have terrible gender and racial biases. My basic understanding is that it has got better in the past 10 years or so, but past scientific literature is littered with inaccuracies that we are still going along with.
I’m thinking drugs specifically, but I suspect it generalizes.
Yeah, there were also several stories where the AI just detected that all the pictures of the illness had e.g. a ruler in them, whereas the control pictures did not. It’s easy to produce impressive results when your methodology sucks. And unfortunately, those results will get reported on before peer reviews are in and before others have attempted to reproduce the results.
To expand on this a bit AI in medicine is getting super good at cancer screening in specific use cases.
People now heavily associate it with LLMs hallucinating and speaking out of their ass but forget about how AI completely destroys people at chess. AI is already getting better than top physics models at weather predicting, hurricane paths, protein folding and a lot of other use cases.
AI’s uses in specific well defined problems with a specific outcome can potentially become way more accurate than any human can. It’s not so much about removing humans but handing humans tools to make medicine both more effective and efficient at the same time.
My favourite story about it was that one time when neural network trained on x-rays to recognise tumors I think, was performing amazingly at study, better than any human could.
Later it turned out that the network trained on real life x-rays with confirmed cases, and it was looking for penmarks. Penmarks mean the photo was studied by several doctors, which mean it’s more likely to be the case that needed second opinion, which more often than not means there is a tumour. Which obviously means that if the case wasn’t studied by humans before, the machine performed worse than random chance.
That’s the problem with neural networks, it’s incredibly hard to figure out what exactly is happening under the hood, and you can never be sure about anything.
And I’m not even talking about LLM, those are completely different level of bullshit
well it’s also that they used biased data. biased data is garbage data. The problem with these neural networks is the human factor, humans tend to be biased, subconsciously or consciously, hence the data they provide to these networks will often be biased as well. It’s like that ML that was designed to judge human faces and it would consistently give non-whites lower scores, because it turned out the input data was mostly full of white faces.
I am convinced that unbiased data doesn’t exist, and at this point I’m not sure it can exist on principal. Then you take your data full of unknown bias, and feed it to a blackbox that creates more unknown bias.
if you get enough data of a specific enough task I’m fairly confident you can get something that is relatively unbiased. Almost no company wants to risk it though because the training would require that no human decisions are made.
Neural networks work very similarly to human brains, so when somebody points out a problem with a NN, I immediately think about whether a human would do the same thing. A human could also easily fake expertise by looking at pen marks, for example.
And human brains themselves are also usually inscrutable. People generally come to conclusions without much conscious effort first. We call it “intuition”, but it’s really the brain subconsciously looking at the evidence and coming to a conclusion. Because it’s subconscious, even the person who made the conclusion often can’t truly explain themselves, and if they’re forced to explain, they’ll suddenly use their conscious mind with different criteria, but they’ll basically always come to the same conclusion as their intuition due to confirmation bias.
But the point is that all of your listed complaints about neural networks are not exclusively problems of neural networks. They are also problems of human brains. And not just rare problems, but common problems.
Only a human who is very deliberate and conscious about their work doesn’t fall into that category, but that limits the parts of your brain that you can use. And it also takes a lot longer and a lot of very deliberate training to be able to do that. Intuition is a very important part of our minds, and can be especially useful for very high level performance.
Modern neural networks have their training data manipulated and scrubbed to avoid issues like you brought up. It can be done by hand, for additional assurance, but it is also automatically done by the training software. If your training data is an image, the same image will be used repeatedly. For example, it will be used in its original format. It can be rotated and used. Cropped and used. Manipulated using standard algorithms and used. Or combinations of those things.
Pen marks wouldn’t even be an issue today, because images generally start off digital, and those raw digital images can be used. Just like any other medical tool, it wouldn’t be used unless it could be trusted. It will be trained and validated like any NN, and then random radiologists aren’t just relying on it right after that. It is first used by expert radiologists simulating actual diagnosis who understand the system enough to report problems. There is no technological or practical reason to think that humans will always have better outcomes than even today’s AI technology.
While the model of a unit in neural network is somewhat reminiscent of the very simplified behaviouristic model of a neuron, the idea that NN is similar to a brain is just plain wrong.
And I’m afraid, based on what you wrote, you didn’t understand what this story means and why I told it.
The important thing to know here is that those AI were trained by very experienced radiologists who are physicians that specialize in reading imaging. The AI’s wouldn’t have this capability if the humans didn’t train them.
Also, the imaging that AI performs well with is fairly specific, and there are many kinds of imaging techniques and diagnostic applications that the AI is still very bad at.
One of the large issues was while they had very good rates of correct diagnosis, they also had higher false positive rates. A false cancer diagnosis can seriously hurt people for example
Iirc the issue was that the researchers left the manufacturer’s logo on the scans.
All of the negative scans were done by the researchers on the same equipment while the positive scans were pulled from various sources. So the AI only learned to identify which scans had the logo.
Yeah this is one of the few tasks that AI is really good at. It’s not perfect and it should always have a human doctor to double check the findings, but diagnostics is something AI can greatly assist with.
My knowledge on this is several years old, but back then, there were some types of medical imaging where AI consistently outperformed all humans at diagnosis. They used existing data to give both humans and AI the same images and asked them to make a diagnosis, already knowing the correct answer. Sometimes, even when humans reviewed the image after knowing the answer, they couldn’t figure out why the AI was right. It would be hard to imagine that AI has gotten worse in the following years.
When it comes to my health, I simply want the best outcomes possible, so whatever method gets the best outcomes, I want to use that method. If humans are better than AI, then I want humans. If AI is better, then I want AI. I think this sentiment will not be uncommon, but I’m not going to sacrifice my health so that somebody else can keep their job. There’s a lot of other things that I would sacrifice, but not my health.
iirc the reason it isn’t used still is because even with it being trained by highly skilled professionals, it had some pretty bad biases with race and gender, and was only as accurate as it was with white, male patients.
Plus the publicly released results were fairly cherry picked for their quality.
Medical sciences in general have terrible gender and racial biases. My basic understanding is that it has got better in the past 10 years or so, but past scientific literature is littered with inaccuracies that we are still going along with. I’m thinking drugs specifically, but I suspect it generalizes.
Yeah, there were also several stories where the AI just detected that all the pictures of the illness had e.g. a ruler in them, whereas the control pictures did not. It’s easy to produce impressive results when your methodology sucks. And unfortunately, those results will get reported on before peer reviews are in and before others have attempted to reproduce the results.
That reminds me, pretty sure at least one of these ai medical tests it was reading metadata that included the diagnosis on the input image.
To expand on this a bit AI in medicine is getting super good at cancer screening in specific use cases.
People now heavily associate it with LLMs hallucinating and speaking out of their ass but forget about how AI completely destroys people at chess. AI is already getting better than top physics models at weather predicting, hurricane paths, protein folding and a lot of other use cases.
AI’s uses in specific well defined problems with a specific outcome can potentially become way more accurate than any human can. It’s not so much about removing humans but handing humans tools to make medicine both more effective and efficient at the same time.
My favourite story about it was that one time when neural network trained on x-rays to recognise tumors I think, was performing amazingly at study, better than any human could.
Later it turned out that the network trained on real life x-rays with confirmed cases, and it was looking for penmarks. Penmarks mean the photo was studied by several doctors, which mean it’s more likely to be the case that needed second opinion, which more often than not means there is a tumour. Which obviously means that if the case wasn’t studied by humans before, the machine performed worse than random chance.
That’s the problem with neural networks, it’s incredibly hard to figure out what exactly is happening under the hood, and you can never be sure about anything.
And I’m not even talking about LLM, those are completely different level of bullshit
well it’s also that they used biased data. biased data is garbage data. The problem with these neural networks is the human factor, humans tend to be biased, subconsciously or consciously, hence the data they provide to these networks will often be biased as well. It’s like that ML that was designed to judge human faces and it would consistently give non-whites lower scores, because it turned out the input data was mostly full of white faces.
I am convinced that unbiased data doesn’t exist, and at this point I’m not sure it can exist on principal. Then you take your data full of unknown bias, and feed it to a blackbox that creates more unknown bias.
if you get enough data of a specific enough task I’m fairly confident you can get something that is relatively unbiased. Almost no company wants to risk it though because the training would require that no human decisions are made.
The problems in thinking that your data is unbiased, is that you don’t know where your data is biased, and you stopped looking
Neural networks work very similarly to human brains, so when somebody points out a problem with a NN, I immediately think about whether a human would do the same thing. A human could also easily fake expertise by looking at pen marks, for example.
And human brains themselves are also usually inscrutable. People generally come to conclusions without much conscious effort first. We call it “intuition”, but it’s really the brain subconsciously looking at the evidence and coming to a conclusion. Because it’s subconscious, even the person who made the conclusion often can’t truly explain themselves, and if they’re forced to explain, they’ll suddenly use their conscious mind with different criteria, but they’ll basically always come to the same conclusion as their intuition due to confirmation bias.
But the point is that all of your listed complaints about neural networks are not exclusively problems of neural networks. They are also problems of human brains. And not just rare problems, but common problems.
Only a human who is very deliberate and conscious about their work doesn’t fall into that category, but that limits the parts of your brain that you can use. And it also takes a lot longer and a lot of very deliberate training to be able to do that. Intuition is a very important part of our minds, and can be especially useful for very high level performance.
Modern neural networks have their training data manipulated and scrubbed to avoid issues like you brought up. It can be done by hand, for additional assurance, but it is also automatically done by the training software. If your training data is an image, the same image will be used repeatedly. For example, it will be used in its original format. It can be rotated and used. Cropped and used. Manipulated using standard algorithms and used. Or combinations of those things.
Pen marks wouldn’t even be an issue today, because images generally start off digital, and those raw digital images can be used. Just like any other medical tool, it wouldn’t be used unless it could be trusted. It will be trained and validated like any NN, and then random radiologists aren’t just relying on it right after that. It is first used by expert radiologists simulating actual diagnosis who understand the system enough to report problems. There is no technological or practical reason to think that humans will always have better outcomes than even today’s AI technology.
While the model of a unit in neural network is somewhat reminiscent of the very simplified behaviouristic model of a neuron, the idea that NN is similar to a brain is just plain wrong.
And I’m afraid, based on what you wrote, you didn’t understand what this story means and why I told it.
The important thing to know here is that those AI were trained by very experienced radiologists who are physicians that specialize in reading imaging. The AI’s wouldn’t have this capability if the humans didn’t train them.
Also, the imaging that AI performs well with is fairly specific, and there are many kinds of imaging techniques and diagnostic applications that the AI is still very bad at.
One of the large issues was while they had very good rates of correct diagnosis, they also had higher false positive rates. A false cancer diagnosis can seriously hurt people for example
Iirc the issue was that the researchers left the manufacturer’s logo on the scans.
All of the negative scans were done by the researchers on the same equipment while the positive scans were pulled from various sources. So the AI only learned to identify which scans had the logo.
Yeah this is one of the few tasks that AI is really good at. It’s not perfect and it should always have a human doctor to double check the findings, but diagnostics is something AI can greatly assist with.
If a doctor is always going to check it, what’s the value of the AI?
If an editor is always going to check an article, what’s the value of a writer?
deleted by creator
Correct. So are lab tech and Doctor.