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Kitchen-Ad-5566 t1_ismuj9v wrote

Because AI in medicine is something new and it is currently getting widespread gradually. Breast cancer detection is actually one of the most promising applications of AI readings in radiology. This is because it is one of the most common cancer types, which lead to screening programs in many countries; so hundreds of millions of breast scans are done annually. And actually, mammography is probably the first imaging type where we will the use of commercial AI products getting widespread.

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Overall-Importance54 OP t1_isnsl90 wrote

Idk, they have has ML breast tissue data sets available since 2010. You an just look one up at https://www.openml.org/search?type=data&sort=runs&id=1465&status=active

I literally just wrote the code and ran the model with the clothing data set. It's sick, in two minutes of coding and training, the model can identify any garment. Putting in the breast tissue data set is just as easy. Show it a scan, it tells you a probability of cancer. 2010 was a long time ago.

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zzzthelastuser t1_isnvnv9 wrote

Similar with self-driving cars, they may work with (made up number) 99% accuracy, but that 1% is still too risky.

Regardless of what the AI says, I would still ask a doctor to see my scan considering a false-negative could cost me my life and a false-positive would probably mean a doctor would double check it anyway.

The bottleneck would still be the person who looks at each scan personally.

That being said, I think there is huge potential in early risk prediction using ML long before a real human could even spot cancer tissue.

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Kitchen-Ad-5566 t1_ispsopg wrote

It isn’t so easy. I mean, you can make something that works more or less. But moving it to a level where it can really be useful clinically will require a huge effort. Because you need to work at extremely low false positive rates while being very sensitive at detection. And, although cancer can be quite obvious in diagnostic scans, in screening scans they are usually very subtle (because screening is done without any symptoms). It will be like looking for a needle in the haystack. And try doing this in that sensitivity/specificity requirements.

I think in general the problem with engineers working on medical topics is that they underestimate the complexity of the problems and the requirement to have in-depth insights about the problems they work on. I get a similar impression from your posts.

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Overall-Importance54 OP t1_isq17oe wrote

I do often underestimate the complexity of a problem. Thank you for your take on this.

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