Raw LLM Responses

I'm not a radiologist, but a machine learning practitioner, so I'll give my perspective. The issue with radiology is that that problem is in principle an ideal "shape" to be tackled by machine learning. The job involves a large amount of transforming data from one form (imaging, patient data) into another form (language), and there's plenty of potential training data being collected every day. In machine learning, progress is often sudden. A problem will go from "almost impossible" to "mostly solved" in a short period of time without much warning. For example, the first computer to win a professional Go match against a human: October 2015. The last human to win a professional Go match against a computer: March 2016. 6 months. A couple of years before, the conventional wisdom was that that this problem was at least a decade away from being solved. If you are planning a career, not just in medicine but in any discipline, and you want to know if your career is at risk of major changes as a result of machine learning, it would be a mistake to look at how researchers are trying to solve that problem today. Few of the methods we use today will be in use 10 years from now, let alone 20. The primary architecture of modern language models was only introduced 5 years ago, for instance. Much of the language modelling research that was done >5 years ago is obsolete. The fact that a problem has not been solved yet means that the state-of-the-art is wrong. Not that the problem itself is insoluble. Instead, you have to look at the fundamentals. How easy is it to train a model? How much interaction with the physical world is essential to the job? If much of the job involves doing things at a computer, the risk is increased. Will human radiologists still exist in the future? Yes of course. Will ML models *one day* gain superiority over human radiologists at a majority of relevant tasks? Certainly. Will this shift occur within the careers of today's radiologists? It seems likely, given the rate of progress in related areas.