I think this is where most of the ML/AI practitioners need to focus on. Instead of obsessing over other accuracy technique for fully autonomous detection they

I think this is where most of the ML/AI practitioners need to focus on. Instead of obsessing over other accuracy technique for fully autonomous detection they should focus on human assisted detection to improve the quality/value of the solution. In this case recall/sensitivity is the utmost important accuracy metric and try to get it closer to 100% if possible. Hence even if the ML/AI got it wrong (due to false positive detection), at least the human experts can have a look at it and hopefully eliminate any false positive. It also considerably reduce the burden of the upstream manual based inspection/automation since it is machine automated. This can also mitigate and hopefully prevent false negative detection because false negative detection will not has the opportunity for further inspection/verification by the human experts or trained inspectors. Essentially the AI/ML is functioning as the filter shifting through the massive data very quickly (with minimum or zero false negative), and then the results are then verified by the much slower human experts.