I totally understand your point #4 - obviously ChatGPT can't do procedures, but I interpreted GP's post as "this is why I did a fellowship in interventional radiology instead of being a (solely) diagnostic radiologist."
But, at the end of the day, diagnostic radiology is about taking an input set of bytes and transforming that to an output set of bytes - that is absolutely what generative AI does excellently. When you said "I'm not sure how you can say this with a straight face?", I couldn't understand if you were talking about now, or what the world will look like in 40 years. Because someone finishing med school now will want to have a career that lasts about 40 years. If anything, I think the present day shortage of radiologists is due to the fact that AI is not there yet, but smart med students can easily see the writing on the wall and see there is a very, very good chance AI will start killing radiology jobs in about 10 years, let alone 40.
As the simplest analogy, we still pay cardiologists to interpret an ECG that comes with a computer readout and is literally a graph of voltages.
First AI will make our lives much easier as it will on other industries, saying it will take 10 years to solve the AI problem for most of diagnostic radiology is laughable. There are many reasons why radiology AI is currently terrible and we don't need to get into them but let's pretend that current DL models can do it today.
The studies you would need to make to validate this across multiple institutions while making sure population drift doesn't happen (see the Epic sepsis AI predicting failure in 2022) and validating long term benefits (assuming all of this is going right) will take 5-10 years. It'll be another 5-10 years if you aggressively lobby to get this through legislation and deal the insurance/liability problem.
Separately w have to figure out how we set up the infrastructure for this presumably very large model in the context of HIPAA.
I find it hard to hard to believe that all of this will happen in 10 years, when once again we still don't have models that do it close to being good enough today. What will likely happen is it will be flagging nodules for me so I don't have to look as carefully at the lungs and we will still need radiologists like we need cardiologists to read a voltage graph.
Radiology is a lot about realizing what is normal, 'normal for this patient' and what we should care about while staying up to date on literature and considering the risks/benefits of calling an abnormality vs not calling one. MRI (other than neuro) is not that old of a field we're discovering new things every year and pathology is also evolving. Saying it's a solved problem of bits and bytes is like saying ChatGPT will replace software engineers in 10 years because it's just copy pasting code from SO or GH and importing libraries. Sure it'll replace the crappy coders and boilerplate but you still need engineers to put the pieces together. It will also replace crap radiologists who just report every pixel they see without carefully interrogating things and the patient chart as relevant.
But, at the end of the day, diagnostic radiology is about taking an input set of bytes and transforming that to an output set of bytes - that is absolutely what generative AI does excellently. When you said "I'm not sure how you can say this with a straight face?", I couldn't understand if you were talking about now, or what the world will look like in 40 years. Because someone finishing med school now will want to have a career that lasts about 40 years. If anything, I think the present day shortage of radiologists is due to the fact that AI is not there yet, but smart med students can easily see the writing on the wall and see there is a very, very good chance AI will start killing radiology jobs in about 10 years, let alone 40.