Eh. Depends on which tech is being used and how. For a lot of things, relatively basic ML models purposefully trained do a pretty good job, and are, in fact, limited by the diagnoses in the training data. But more generalized “AI” tools seem rather… questionable.
Like, you can train a SVM on fMRIs to compare structures in the brain between patients diagnosed with bipolar disorder and those that are not diagnosed with it, and it will have an accuracy rate on new patients basically equal to the accuracy rate of the doctors who did the diagnosing in the training set. But you’ll have a much harder time creating a model that takes in fMRIs and reports back answers to the question of “which brain disease or abnormality do I have?”
This stuff works much closer to advertised when it’s narrowly defined and purpose built, but the people making and funding this work want catch-all doctor replacements, because of course they do, because there’s way more money in charging hospitals and patience 10% less than a doctor’s salary than there is in providing tools that make doctors’ efforts in diagnosing specific illnesses easier.
Or, at least there is if you can pull it off.
MalReynolds@slrpnk.net 7 months ago
Really.
Daxtron2@startrek.website 7 months ago
Says all you need to know about their opinion lol
Ludrol@szmer.info 7 months ago
Still AI misalignment is a real issue. I just don’t remember which model was studied and had been found out that it was missaligned.
Daxtron2@startrek.website 7 months ago
That and bias, absolutely need improvements. That doesn’t mean LLMs can’t be extremely effective if given appropriate tasks. The problem is that the people who make decisions about where they’re used aren’t technical enough to understand their strengths and limitations