I actually have a bet with a radiologist that AI will be as good or better than radiologists at reading images in a fairly near future.
The reason I think this (as a non-specialist) is this: when I upload a random photo to Google or Facebook, those systems recognize the faces of people in those pictures without any prompt. This seems like a much more constrained problem (given a pretty specific set of images of chest cavities, state the probability of a cancerous tumor.)
I am guessing that there's nothing inherent in this problem that is much more difficult than other image recognition problems. I suspect that this outcome is because we have fairly new technology competing against highly specialized humans doing a very specific task and doing it well.
I suspect that will happen is that the technology will relatively quickly catch up to humans and do equally well, after which point it's just a speed of response and economics question, where the technology wins over the person.
I'm with @greazy on this one. The assumption made is that a set of labeled images is enough. Was the training set validated against a diagnosis based on biopsy of some kind? I.e. the images that were trained on, marked by some radiologist (more likely multiple radiologists), how were they validated to contain cancer or be cancer free? Did someone follow up a few years later and confirm the diagnosis was correct?
Also, different modalities will produce different quality images, how was that accounted for in training models? Did they use all the images for a single scan or a subset of the images of a single scan?
The problem is you're trying to train a model where you have many images for a single scan, like slices. Depending on the modality, you'll get different resolutions, different "visual" inclusions, etc. etc. So labeling the individual images and labeling the entire collection is really hard.
> I am guessing that there's nothing inherent in this problem that is much more difficult than other image recognition problems.
This is factually wrong. You're assumption I guess is that its a data issue: if we can just get more data we'd solve this issue.
The reality is that these diseases are complex, their presentation is complex, and compounded by different technology. There's also the issue of data complexity, x-rays contain less information compared to a picture of your dog.
You've misunderstood my point I think. How a disease presents is different issue than the complexity of faces. A photo of a face has more information encoded than an x-ray image. This lack of information is part of the problem that I think more data doesn't solve and why humans are able to identify cancer. We have better image recognition 'software'. And that's my point, better algos, not more data
The reason I think this (as a non-specialist) is this: when I upload a random photo to Google or Facebook, those systems recognize the faces of people in those pictures without any prompt. This seems like a much more constrained problem (given a pretty specific set of images of chest cavities, state the probability of a cancerous tumor.)
I am guessing that there's nothing inherent in this problem that is much more difficult than other image recognition problems. I suspect that this outcome is because we have fairly new technology competing against highly specialized humans doing a very specific task and doing it well.
I suspect that will happen is that the technology will relatively quickly catch up to humans and do equally well, after which point it's just a speed of response and economics question, where the technology wins over the person.