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I think there's a huge potential for patient benefit if this is done correctly, though I worry that we'll just create mostly adequate classifiers based on exist

by gbrown 7y ago

I think there's a huge potential for patient benefit if this is done correctly, though I worry that we'll just create mostly adequate classifiers based on existing data. I also think we need to watch out for the human-attention issues illustrated in almost self driving cars. If a radiologist gets used to the computer being right 9/10 times, they could miss the 10th which would usually have been caught. Overall we need more CV/ML/AI (choose your acronym) in this space, but it definitely requires some care.

m_ke 7y ago
There's a ton of deep learning startups working in this space already. Hardest part for most of them is getting access to training data. Because of that we'll probably see most of the commercial innovation coming from China and other countries that don't care about privacy. Doctors make mistakes all the time and people in the medical profession work odd hours so there's already a ton of room for errors. Having a machine check their work or provide a second opinion will help them a lot. Having a system that can do the primary screening and prioritize patients before a radiologist is available will save a ton of lives.
- gbrown 7y ago
  Part of the problem is that inadequate training data may not be a barrier to successfully marketing and implementing these systems - the tech is sexy and people are rightly excited. > Doctors make mistakes all the time and people in the medical profession work odd hours so there's already a ton of room for errors. That's my point - this is the training data. Unless we're careful, we're just going to approximate what we're already doing.
  - m_ke 7y ago
    These systems can be trained on more data than any doctor will see in their lifetime. This means that it can pick up on discrepancies between doctors and has room to outperform most doctors. I'm working on a similar system but for dietaty feedback based on images and it's amazing to see the model outperform all of the dietitians because it's able to see how all of the coaches respond to similar items.

by gbrown 7y ago

m_ke 7y ago

There's a ton of deep learning startups working in this space already. Hardest part for most of them is getting access to training data. Because of that we'll probably see most of the commercial innovation coming from China and other countries that don't care about privacy. Doctors make mistakes all the time and people in the medical profession work odd hours so there's already a ton of room for errors. Having a machine check their work or provide a second opinion will help them a lot. Having a system that can do the primary screening and prioritize patients before a radiologist is available will save a ton of lives.

gbrown 7y ago
Part of the problem is that inadequate training data may not be a barrier to successfully marketing and implementing these systems - the tech is sexy and people are rightly excited. > Doctors make mistakes all the time and people in the medical profession work odd hours so there's already a ton of room for errors. That's my point - this is the training data. Unless we're careful, we're just going to approximate what we're already doing.
- m_ke 7y ago
  These systems can be trained on more data than any doctor will see in their lifetime. This means that it can pick up on discrepancies between doctors and has room to outperform most doctors. I'm working on a similar system but for dietaty feedback based on images and it's amazing to see the model outperform all of the dietitians because it's able to see how all of the coaches respond to similar items.