Earlier quoted context omitted.
> Anyone who can build an ML model to catch these clues is going to make billions. You'd be surprised. IBM poured billions into Watson and appears to have been pretty successful in nearly reaching parity with a certified oncologist, but the results were dismissed because it didn't outperform them. > At first, Manipal used Watson to recommend treatment options for all cancer patients, said oncologist S.P. Somashekhar.…
That's not a great definition of parity. We'd want accuracy and specificity numbers linked to outcomes, not concurrence. The times when Watson agrees with doctors is effectively irrelevant - results would be the same whether or not he was added. We need to highlight whether, given a disagreement, Watson was better or worse for outcomes.
Given a fixed number of oncologists and deploying Watson only to support those oncologists, yes, you're correct that its only useful if it outperforms them. But I think of it more like Watson is a single hive-mind team of like a thousand med students near the end of residency: they get most things right but there are a few places where more experienced doctors will be better, though the scale with the hive mind is far higher.
You have one of two reactions to that. 1) Hire fewer senior oncologists and have them focus on the more difficult cases and leave the hive-mind to deal with thousands of routine cases, or 2) ignore the hive mind until its literally better than a typical senior oncologist.
The medial profession seems to repeat this cycle of "only full doctors can do anything because even seemingly routine cases might be hiding something more serious" to "maybe some routine things can be done by people with less training and full doctors should focus on the more difficult cases". See nurse practitioners, dental assistants, and, in my mind at least where we are going with things like Watson.