Screening is hard work and tedious, so even trained professionals regularly miss things. TP incidence rate is under 1% in the screening population.

There have been studies showing significant improvement from double-reading mammo, for example (i.e. two radiologists, independently). Using an ML approach is trying to give you some or all of this benefit without the cost of redundant reads.

>then what is the point of the algo?

The point is that the algorithm can improve results. This isn't ad placement, it's peoples' lives. Checking and double checking should be the norm.

Better to implement a system with a high rate of false positives (more importantly, low rate of false negatives) from the machine learning component, with all positive findings passed onto the radiologist. If the system can reliably (big if) filter out 98% of the chaff then the radiologist can spend a lot more time separating the false positives from the true positives. This approach has worked well for me so far.

This approach is problematic in medical screening applications. Mainly because you don't want to increase the work up rate for false positives since if they involve biopsy and a large screening population, eventually you will kill people this way (indirectly) so there is a pressure to control FP rate.

Because the scan check by the radiologist becomes a _double_ check.