I have no idea why everyone tries to solve the hardest problem (true positives, no false positives) with machine learning, instead of solving the much easier problem (true negatives, no false negatives) that often delivers similar value.

E.g. If a system could tell me which 967 results out of 1000 are normal, and send the other 33 for human review.

True positives require solving every problem in the solution space. True negatives only require solving the most frequent problems.