[napoleoncomplex]

There is a tremendous share of medicine specialties facing shortages, and fear of AI is not a relevant trend causing it. Even the link explaining shortages in the above article is pretty clear on that.

I do agree with the article's author's other premise, radiology was one of those fields that a lot of people (me included) have been expecting to be largely automated, or at least the easy parts, as the author mentions, and that the timelines are moving slower than expected. After all, pigeons perform similarly well to radiologists: https://pmc.ncbi.nlm.nih.gov/articles/PMC4651348/ (not really, but it is basically obligatory to post this article in any radiology themed discussion if you have radiology friends).

Knowing medicine, even when the tech does become "good enough", it will take another decade or two before it becomes the main way of doing things.

[bachmeier]

The reason AI is hyped is because it's easy to get the first 80% or 90% of what you need to be a viable alternative at some task. Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years. But the low-hanging fruit is easy and fast. It may never be feasible to complete the last few percent. Then it changes from "AI replacement" to "AI assisted". I don't know much about radiology, but I remember before the pandemic one of the big fears was what we'd do with all the unemployed truck drivers.

[candiddevmike]

> Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years

That's a hefty assumption, especially if you're including accuracy.

[hnthrowaway6543]

> That's a hefty assumption, especially if you're including accuracy.

That's exactly what the comment is saying. People see AI do 80% of a task and assume development speed will follow a linear trend and the last 20% will get done relatively quickly. The reality is the last 20% is hard-to-impossible. Prime example is self-driving vehicles, which have been 80% done and 5 years away for the past 15 years. (It actually looks further than 5 years away now that we know throwing more training data at the problem doesn't fix it.)

[ninetyninenine]

We are at 95 percent now for self driving. I regularly use Waymo robot cars in sf. No driver. The gap is now just scaling.

Take what is 100% complete in one city and do it in another city.

Problem was solved… you just missed the boat.

[hnthrowaway6543]

Waymo barely works, with 24/7 monitoring by humans in a "fleet response" center[0], in 4 cities in the world. That's only 95% done if you're counting good enough for government work.

[0] https://waymo.com/blog/2024/05/fleet-response/

[soco]

The monitoring might be 24/7 but its reaction time is nothing usable in a life-and-death situation. Or I just cannot imagine a human being notified "I think I'm crashing into something" and able to take over and do anything of significance within that second to avoid the crash (except hitting on the brakes which the car could do just as well). So don't read too much into the response team, it has definitely its use but won't save you from plunging into that sinkhole who just appeared.

[bdndndndbve]

OP is being facetious, the "last 20%" is a common saying implying that you've back-loaded the hard part of a task.

[rsynnott]

That's their point, I think; since the 50s or so, people have been making this mistake about AI and AI-adjacent things, and it never really plays out. That last '10%' often proves to be _impossible_, or at best very difficult; you could argue that OCR has managed it, finally, at least for simple cases, but it took about 40 years, say.

[edanm]

> The reason AI is hyped is because it's easy to get the first 80% or 90% of what you need to be a viable alternative at some task.

No, it's because if the promise of certain technologies is reached, it'd be a huge deal. And of course, that promise has been reached for many technologies, and it's indeed been a huge deal. Sometimes less than people imagine, but often more than the naysayers who think it won't have any impact at all.

[pavel_lishin]

> Extrapolating in a linear fashion, AI will do the last 10-20% in a few months or maybe a couple years

Extrapolating in a linear fashion, in a few years my child will be ten foot tall, weight six hundred pounds, and speak 17 languages.

The first 90% is the easy part. It's the other 90% that's hard. People forget that, especially people who don't work in software/technology.

[bonoboTP]

> There is a tremendous share of medicine specialties facing shortages

The supply of doctors is artificially strapped by the doctor cartel/mafia. There are plenty who want to enter but are prevented by artificial limits in the training.

[mananaysiempre]

Medical professionals are highly paid, thus an education in medicine is proportionally expensive. An education in medicine is expensive, thus younger medical professionals need to be highly paid in order to afford their debt. Until the vicious cycle is here is broken (e.g. less accessible student loans? and more easily defaultable is one way to spell less accessible), things are not going to improve. And there’s also the problem that you want your doctors to be highly paid, because it’s a stressful, high-responsibility job with stupidly difficult education.

[bonoboTP]

US doctors are ridiculously overpaid compared to the rest of the developed world, such as the UK or western EU. There's no evidence that this translates to better care at all. It's all due to their regulatory capture. One possible outcome is that healthcare costs continue to balloon and eventually it pops and the mafia gets disbanded and more immigrant doctors will be allowed to practice, driving prices to saner levels.

[tzs]

How doctors are licensed in the US compared to western Europe might explain why health care costs are higher in the US, but it does not explain why health care costs are rising so much. That's because health care costs are rising at similar rates in western Europe (and most of the rest of the first world).

For example from 2000 to 2018 here's the ratio of per capita health care costs in 2018 to the costs in 2000 for several countries:

2.1 Germany 1.8 France 2.0 Canada 1.7 Italy 2.6 Japan 2.6 UK 2.3 US

Here's cost ratios over several decades compared to 1970 costs for the US, the UK, and France:

     1980 1990 2000 2010 2020
  US  3.2  8.2 13.9 24.1 36.3
  UK  3.1  6.3 15.3 27.8 40.5
  FR  3.4  7.6 14.9 21.1 28.5

Here's the same data showing the the cost ratio decade to decade instead of from 1970:

     1980 1990 2000 2010 2020
  US  3.2  2.6  1.7  1.7  1.5
  UK  3.1  2.0  2.4  1.8  1.5
  FR  3.4  2.2  2.0  1.4  1.4

My data source was https://data.oecd.org/healthres/health-spending.htm but it looks like data.oecd.org reorganized their site so that redirects to https://www.oecd.org/en/data/indicators/health-spending.html which seems to have the data but with a much more limited interface.

[bonoboTP]

Doctor salary is not the only or perhaps even the main factor in healthcare expensiveness, but taking on the overall cost disease in healthcare would broaden the scope too wide for this thread, I think.

Also, I admit that the balloon may in fact never pop, since one theory says that healthcare costs so much simply because it can. It just expands until it costs as much as possible but not more. I'm leaning towards accepting Robin Hanson's signaling-based logic to explain it.

[singleshot_]

The supply of doctors would be much greater if incompetent people were allowed into the training pathway, for sure.

[bonoboTP]

Yep, this is precisely what they argue. They don't simply say they want to keep their high salary and status due to undersupply. They argue that it's all about standards, patient safety etc. In the US, even doctors trained in Western Europe are kept out or strangled with extreme bureaucratic requirements. Of course, again the purported argument is patient safety. As if doctors in Europe were less competent. Health outcome data for sure doesn't indicate that, but smoke and mirrors remain effective.

[DaveExeter]

Bad news...they're already there!

[infecto]

I wouldn’t dismiss the premise so quickly. Other factors certainly play a role, but I imagine that after 2016, anyone considering a career in radiology would have automation as a prominent concern.

[randomdata]

Automation may be a concern. Not because of Hinton, though. There is only so much time in the day. You don't become a leading expert in AI like Hinton has without tuning out the rest of the world, which means a random Average Joe is apt to be in a better position to predict when automation is capable of radiology tasks than Hinton. If an expert in radiology was/is saying it, then perhaps it is worth a listen. But Hinton is just about the last person you are going to listen to on this matter.

[caeril]

> even when the tech does become "good enough", it will take another decade or two before it becomes the main way of doing things.

What you're advocating for would be a crime against humanity.

Every four years, the medical industry kills a million Americans via preventable medical errors, roughly one third of which are misdiagnoses that were obvious in hindsight.

If we get to a point at which models are better diagnosticians than humans, even by a small margin, then delaying implementation by even one day will constitute wilful homicide. EVERY SINGLE PERSON standing in the way of implementation will have blood on their hands. From the FDA, to HHS, to the hospital administrators, to the physicians (however such a delay would play out) - every single one of them will be complicit in first-degree murder.