My Dad is a consultant radiologist, recently retired but still doing some part time work. When I've had conversations about whether he thinks his job might be a

My Dad is a consultant radiologist, recently retired but still doing some part time work. When I've had conversations about whether he thinks his job might be automated, he says that the main problem he's seen with current ML systems is that they throw up far too many false positives. They also aren't great for unusual or corner cases. For example, in one scan he saw recently the very corner of the picture was occluded because the radiographer had left something on the machine (not quite sure what). He said most trainees would have just ignored it, but he sent it back. Turns out it was hiding a tumour. Now I know this is just one example, but he said you'd be surprised by the number of weird things that turn up like that. Where he does see it having a place is to help radiologists from missing really obvious things because they're tired. Most people don't realise how much concentration is required to just look at scan after scan for miniscule clues indicating a potential problem.