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.