4 ms·

Are AI models able to detect abnormalities that even an experienced radiologist can't see? i.e. something that would look normal to a human eye but AI correctly

by janice1999 1y ago

Are AI models able to detect abnormalities that even an experienced radiologist can't see? i.e. something that would look normal to a human eye but AI correctly flags it for investigation? Or are all AI detections 'obvious' to human eyes and simply a confirmation? I suspect the latter since it was human annotated images the model was trained on.

seesthruya 1y ago
Depends on what you mean by 'see'. For example, let's say I'm looking at a chest x-ray. There is a pneumonia at the left lung base and I am clever enough to notice it. 'Aha', I think, congratulating myself at making the diagnosis and figuring out why the patient is short of breath. But, in this example, I stop looking closely at the X-ray after noticing the pneumonia, so I miss a pneumothorax at the right lung apex. I have made a mistake radiologists call 'satisfaction of search'. My 'search' for the patient's problem was 'satisfied' by finding the pneumonia, and because I am human and therefore fundamentally flawed, I stopped looking for a second clinically relevant diagnosis. An AI module that detects a pneumothorax is not prone to this type of error. So it sees something I did not. But it doesn't see something that I can't see. I just didn't look.
- ceejayoz 1y ago
  This is definitely a thing. https://www.npr.org/sections/health-shots/2013/02/11/171409656/why-even-radiologists-can-miss-a-gorilla-hiding-in-plain-sight https://www.npr.org/sections/health-shots/2013/02/11/1714096... I'm skeptical to the claim that AI isn't prone to this sort of error, though. AI loves the easy answer.
  - the_sleaze_ 1y ago
    AI is overloaded. An LLM loves the easy answer, but that's not what is underlying an image classification model.
- alabastervlog 1y ago
  > I have made a mistake radiologists call 'satisfaction of search'. Ah, now I have a name for it. When I've chased a bug and fixed a problem I found that would cause the observed problem behavior, but haven't yet proven the behavior is corrected, I'm always careful to specify that "I fixed a problem, but I don't know if I fixed the problem". Seems similar: found and fixed a bug that could explain the issue, but that doesn't mean there's not another one that, independently, would also cause the same observed problem.
  - ceejayoz 1y ago
    It's also called inattentional blindness. https://en.wikipedia.org/wiki/Inattentional_blindness https://en.wikipedia.org/wiki/Inattentional_blindness
bilbo0s 1y ago
I've been going to RSNA for over 25 years, in all that time, the best I've seen from any model presented to me was the smack the radiologist on the head and say, "you dummy, you should have seen that!" model. That is, the models spot pathologies that 99.9999% of rads would spot anyway if not overworked, tired, or in a hurry. But, addressing the implication of your question, the value is actually in spotting a pathology that 99.9999% of rads would never spot. In all my years developing medical imaging startups and software, I've never seen it happen. I don't expect to see it in my lifetime.
- seesthruya 1y ago
  I have a fairly strong background in tech, and I've been programming computers since 1979 when my dad bought me a TRS-80. Tape drives FTW! I agree with almost everything you've said here. Except 'not in my lifetime', because I plan on living for a very long time, and who knows what those computer nerds will come up with eventually ;-)
- SketchySeaBeast 1y ago
  I'm sure it's a matter of training data, but I don't know if it's a surmountable problem. How do you get enough training data for the machine to learn and reliably catch those exceptions?
- nearbuy 1y ago
  One Harvard study trained an AI that could reliably determine someone's race from a chest X-ray. AIs can be trained to see things we can't. The difficulty is likely in making a good training dataset of labeled images with pathologies radiologists couldn't see. I imagine in some cases (like cancer), you may happen to have an earlier CT scan or X-ray from the patient where the pathology is not quite yet detectable to the human eye.
  - devilbunny 1y ago
    I suspect that radiologists could identify race from a plain chest x-ray if they were given the patient’s race and asked to start noticing the difference. They just aren’t doing it because, if it’s important, you can just look at the patient. There are a lot of things in medicine that aren’t in literature, but are well-known among certain practitioners. I’m an anesthesiologist and practice in an area with a large African-American population. About 10-15% (rough guess) of people of West African descent will have a ridiculously strong salivary response to certain drugs (muscarinic agonists). As in, after one dose their mouths will be full of saliva in seconds. We don’t have East Africans for comparison, so I can’t say it’s a pan-Bantu thing, but I have seen it in a Nigerian who lived here. Not in the literature, but we all know it. I had a (EDIT: non-anesthesia) colleague ask me about a hypersecretory response from such a drug. I said, oh, was he black? Yes, how did I know? Because we give those drugs all the time and have eyes. It’s very rare to see in European-descended populations.

seesthruya 1y ago

Depends on what you mean by 'see'. For example, let's say I'm looking at a chest x-ray. There is a pneumonia at the left lung base and I am clever enough to notice it. 'Aha', I think, congratulating myself at making the diagnosis and figuring out why the patient is short of breath. But, in this example, I stop looking closely at the X-ray after noticing the pneumonia, so I miss a pneumothorax at the right lung apex. I have made a mistake radiologists call 'satisfaction of search'. My 'search' for the patient's problem was 'satisfied' by finding the pneumonia, and because I am human and therefore fundamentally flawed, I stopped looking for a second clinically relevant diagnosis. An AI module that detects a pneumothorax is not prone to this type of error. So it sees something I did not. But it doesn't see something that I can't see. I just didn't look.

ceejayoz 1y ago
This is definitely a thing. https://www.npr.org/sections/health-shots/2013/02/11/171409656/why-even-radiologists-can-miss-a-gorilla-hiding-in-plain-sight https://www.npr.org/sections/health-shots/2013/02/11/1714096... I'm skeptical to the claim that AI isn't prone to this sort of error, though. AI loves the easy answer.
- the_sleaze_ 1y ago
  AI is overloaded. An LLM loves the easy answer, but that's not what is underlying an image classification model.
alabastervlog 1y ago
> I have made a mistake radiologists call 'satisfaction of search'. Ah, now I have a name for it. When I've chased a bug and fixed a problem I found that would cause the observed problem behavior, but haven't yet proven the behavior is corrected, I'm always careful to specify that "I fixed a problem, but I don't know if I fixed the problem". Seems similar: found and fixed a bug that could explain the issue, but that doesn't mean there's not another one that, independently, would also cause the same observed problem.
- ceejayoz 1y ago
  It's also called inattentional blindness. https://en.wikipedia.org/wiki/Inattentional_blindness https://en.wikipedia.org/wiki/Inattentional_blindness

bilbo0s 1y ago

I've been going to RSNA for over 25 years, in all that time, the best I've seen from any model presented to me was the smack the radiologist on the head and say, "you dummy, you should have seen that!" model. That is, the models spot pathologies that 99.9999% of rads would spot anyway if not overworked, tired, or in a hurry. But, addressing the implication of your question, the value is actually in spotting a pathology that 99.9999% of rads would never spot. In all my years developing medical imaging startups and software, I've never seen it happen. I don't expect to see it in my lifetime.

seesthruya 1y ago
I have a fairly strong background in tech, and I've been programming computers since 1979 when my dad bought me a TRS-80. Tape drives FTW! I agree with almost everything you've said here. Except 'not in my lifetime', because I plan on living for a very long time, and who knows what those computer nerds will come up with eventually ;-)
SketchySeaBeast 1y ago
I'm sure it's a matter of training data, but I don't know if it's a surmountable problem. How do you get enough training data for the machine to learn and reliably catch those exceptions?
nearbuy 1y ago
One Harvard study trained an AI that could reliably determine someone's race from a chest X-ray. AIs can be trained to see things we can't. The difficulty is likely in making a good training dataset of labeled images with pathologies radiologists couldn't see. I imagine in some cases (like cancer), you may happen to have an earlier CT scan or X-ray from the patient where the pathology is not quite yet detectable to the human eye.
- devilbunny 1y ago
  I suspect that radiologists could identify race from a plain chest x-ray if they were given the patient’s race and asked to start noticing the difference. They just aren’t doing it because, if it’s important, you can just look at the patient. There are a lot of things in medicine that aren’t in literature, but are well-known among certain practitioners. I’m an anesthesiologist and practice in an area with a large African-American population. About 10-15% (rough guess) of people of West African descent will have a ridiculously strong salivary response to certain drugs (muscarinic agonists). As in, after one dose their mouths will be full of saliva in seconds. We don’t have East Africans for comparison, so I can’t say it’s a pan-Bantu thing, but I have seen it in a Nigerian who lived here. Not in the literature, but we all know it. I had a (EDIT: non-anesthesia) colleague ask me about a hypersecretory response from such a drug. I said, oh, was he black? Yes, how did I know? Because we give those drugs all the time and have eyes. It’s very rare to see in European-descended populations.