In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’.
If we had followed every AI evengelist sugestion the world would have collapsed.
In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’.
If we had followed every AI evengelist sugestion the world would have collapsed.
People love to bring this up, and it was a silly thing to say -- particularly since he didn't seem to understand that radiologists only spend a small part of their time reading scans.
But he said it in the context of a Q&A session that happened to be recorded. Unless you're a skilled politician who can give answers without actually saying anything, you're going to say silly things once in a while in unscripted settings.
Besides that, I'd hardly call Geoffrey Hinton an AI evangelist. He's more on the AI doomer side of the fence.
No, this was not an off-hand remark. He made a whole story comparing the profession to the coyote from road runner “they’ve already run of the cliff but don’t even realize it”. It was callous, and showed a total ignorance of the fact that medicine might be more than pixel classification.
Radiologists, here, mostly sit at home, read scan and dictate reports. They rarely talk to other doctors and talking to a patient is beyond them. They are some of the specialists with the best salary.
With interventional radiologists and radio-oncologists it's different but were talking about radiologists here...
I'm a radiologist and spend 50% of my time either talking to patients or other clinicians.
You practice in Québec ? If so I am quite surprised, because my wife had a lot of scans and we never met a radiologists who wasn't a radio-oncologist. And her oncologist never talked with the radiologists either. The communication between them was always through written demands and reports. And the situation is similar between her neurologist and the radiologists.
By the way, even if I sound dismissive I have great respect for the skills required by your profession. Reading an IRM is really hard when you have the radiologist report in hand and to my untrained eyes it's impossible without it!
And since you talk to patients frequently, I have an even greater respect of you as a radiologist.
My wife’s an ER doctor and she talks to radiologists all the time.
I also recently had surgery and the surgeon consulted with the radiologist that read my MRI before operating.
Then it's an organizational problem (or choice) in the specific hospital where my wife is treated/followed and I apologize to all radiologists that actually talk to peoples in a professional capacity!
Or maybe it's related to socialized Healthcare because in the article there is a breakdown of the time spent by a radiologists in Vancouver and talking to patients isn't part of it.
I would argue an "AI doomer" is a negatively charged type of evangelist. What the doomer and the positive evangelist have in common is a massive overestimation of (current-gen) AI's capabilities.
Many of us have changed opinions after seeing how the tech does not scale.
At the time? I would say he was a AI evangelist.
The tech scales, but accessing the training data is a real problem. It's not like scraping the whole internet. And most of it is unlabeled.
I think in general this lack affects almost all areas of human endeavor. All my speech teaching my kids how to think clearly, to young software engineers about how to build software in a some giant ass bureaucracy, how to debug some tricky problem, none of that sort of discovering truth one step at a time or teaching new stuff is in blogs or anything outside the moment.
When I do write something up, it is usually very finalized at that time; the process of getting to that point is not recorded.
The models maybe need more naturalistic data and more data from working things out.
If you need more data to scale, and there is no data, it literally can't scale.
Scale is not always about trougput. You can be constrained by many things, in this case, data.
I was unclear. There is data, but it is expensive to access, so the value proposition is often not there without some beneficent entity.
It's the power of confidence and credentials in action. Which is why you should, when possible, look at the underlying logic and not just the conclusion derived from it. As this catches a lot of fluff that would otherwise be Trojan-Horsed into your worldview.
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Let's assume the last person that entered their radiologist training started then and the training lasts 5 years. At the end of their training the year is 2021 and they are around 31. So that means they will practice medicine for cca 30 years which would put the calendar at around 2051. I'd wager in 25 years we'd get there so I think his opinion still has a large percentage of being correct.
And if it doesn't work out ?
People can't tell what they'll eat next sunday but they'll predict AGI and singualrity in 25 years. It's comfy because 25 years seems like a lot of time, it isn't.
https://en.wikipedia.org/wiki/List_of_predictions_for_autono...
> I'd wager in 25 years we'd get there so I think his opinion still has a large percentage of being correct.
What percent, and which maths and facts let you calculate it ? The only percent you can be sure about is that it's 100% wishful thinking
I mean it's an opinion (mine) so maybe feel free to disagree with me without going overboard.
> It's comfy because 25 years seems like a lot of time, it isn't.
I don't know how old you are but life 25 years ago from a tech perspective was *very* different.
Different, but nobody could predict what would happen next. We know now how different it was, but we didn’t know back then how it would be different now. There were people/companies who were right, and more people who weren’t. I had good predictions, and bad predictions. I didn’t understand why people didn’t use their phone already like how they use smartphones now. You could do everything what you can do now (except things which were discovered since then, mainly ML stuffs). Browse the internet (it was always interesting how people didn’t know what was WAP), listen to music, read books, play games, run random apps (there was waaaay more freedom regarding this back then by default, people just didn’t know how). But still, we needed smartphones. That was the thing which crossed the line for normies, and for most of them only more than 5 years after iPhone was released. My prediction of convergence would have failed without the modern smartphone, which I couldn’t foresee. It was pure luck. We needed a breakthrough.
That doesn’t mean that you can’t predict anything with high certainty. You just don’t know whether the status quo will be disturbed. And when you need a status quo disturbance for your prediction, you’re in pure luck category. When your prediction requires lack of status quo changes, then your prediction is safer. And of course sorter the term the better. When ChatGPT came out, Cursor and Claude Code could be predicted, I predicted them. Because no changes in status quo was required and it was a short term prediction. But if there would have been a new breakthrough, then those wouldn’t have been created. When they predicted fully self driving cars, or less people checking X-rays, you needed a status quo change: legal first, but in case of general, fully self driving cars, even technical breakthroughs. Good luck with that.
Let’s say we do manage to develop a model that can replace radiologists in 20 years. But we stop training them today. What happens 15 years from now when we don’t have nearly enough radiologists.
I'm not saying it's a good idea to think like that, I'm just saying I'd wager he's right on thinking that AI will be in a good position in 20+ years.
Radiologists can retrain to do something else adjacent surely? Not like they'll suddenly be like an 18 year old with no degree trying to find a job.
Why do we assume that radiologists would have literally 0% involvement in the radiology workflow?
I could see the assumption that one radiologist supervises a group of automated radiology machines (like a worker in an automated factory). Maybe assume that they'd be delegated to an auditing role. But that they'd go completely extinct? There's no evidence of, even historically, a service being consumed that has zero human intervention.
Alarm clocks. Elevators. ATMs. Laundry. Chess opponent. Watching a movie. …
> I'd wager
Maybe don't?
So all the current radiologists are going to live until 2051?
Even Marie Curie would have.
Why you write 2021? It clearly says 2016.
I mean if you change the data to fit your argument you will always make it look correct.
Lets assume we stop in 2016 like he said, where do we get the 1000 radiologist the US needs a year?
> the training lasts 5 years. At the end of their training the year is 2021
The training lasts 5 years, 2021 - 5 = 2016 If they stopped accepting people into the radiologist program but let people already in to finish, then you would stop having new radiologist in 2021.
Training is a lot longer than that in Québec, radiology is a specialty, so they must first do their 5 years in medicine, followed by a 5 year diagnostic radiology residency program. And it's frequently followed by a 2 years fellowship.
So 5 + 5 + [0,2] is [10,12] years of training.
Residents are working doctors, so we’d start losing useful work the year we stop taking new residents.
'people should stop training radiologists now'
That sentence and what you wrote are not 100% the same.
Look, if we were okay with tolerating less regulation in medicine, and dismantled AMA, Hinton would have proven to be right by now and everyone would have been happier
Definitely an aggressive timeline but it seems like the biggest barrier to AI taking over radiology will be legal. Spending years training for a job which only continues to exist because of government fiat, which could change at any time, seems like a risky choice.
Too damn hard to predict the future! We live in an age where 20 years is unseeable for a lot of things.