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"We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists." It's been 9 years since Nobel laureate and AI guru Geoffrey Hinton made this statement. Over those 9 years, my workload has only increased. Yesterday I...

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David Jacobs

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36,201 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 11

Фото профиля Maple Manthan
Maple Manthan1 год назад

Even if AI is very comparable to a well trained radiologist, we need expertise in PEOPLE PEOPLE can teach others and eventually improve AI Human + AI >> AI alone >> human alone, even when AI Is better than human

Фото профиля Hector Resendez - Trade School Secrets
Hector Resendez - Trade School Secrets2 лет назад

The college days are coming to an end soon. 🚀 Fun Fact: Our graduates are making serious bank! 💸 💡 HVAC: $32/hr = $66,560/year 🔌 Electrician: $29/hr = $60,320/year 🚽 Plumber: $27/hr = $56,160/year All from our 100% online courses that take just 100-120 hours to complete! 🤯 Most students finish in under 3 months. 🏎️ The ROI on these trades is 🔥 compared to college. Prove me wrong! 😜

Фото профиля JamesTor
JamesTor1 год назад

Never forget that 50 yrs ago we were told to expect flying cars by now

Фото профиля ajc
ajc1 год назад

Can AI help leverage your expertise vs. replacing it?

Фото профиля Johnsemalhuis
Johnsemalhuis1 год назад

Get your laptop to embolize you liver mass or barbotage your shoulder and then get back to me Mr Coder. There are numerous studies proving coding and program bias in AI's. AI may change the way we practice but we we will not be redundant any time soon. "Clinical correlation required" will still be in the AI reports...

Фото профиля Canada=Liberal Government's 2025 Sh*t Hole!
Canada=Liberal Government's 2025 Sh*t Hole!1 год назад

They are not getting it, the human experience is not this world, AI to assist cool but ai replacement? no thx. Radiologists are impressive. Urgent CTs save lives, imagine a hot stroke comes in and the AI crashes or fails? No one will be able to determine whether its an embolic stroke vs a massive bleed even if u can still do the imaging? No tpa and no chance at resolution of stroke symptoms. U r right, not safe. No one does RAD residency, eventually not one human on earth who cannot diagnose Xrays, CTs and MRIs manually. Reminds me of all the pilots crashing small air crafts, because they are so dependent on auto pilot.

Фото профиля Rafael Gomez
Rafael Gomez1 год назад

One assumes AI could be trained to detect "anomalies" and flag them in results, shortening your workflow since I know when I've seen reports from procedures and tests they can sometimes include hundreds of images that I assume some radiologist needs to troll through but I don't know if I'd trust results that were 100% AI driven. Just like I'm not 100% certain I trust AI "self driven" cars regardless of how great everyone claims the tech is. In the end tech is only as good as its creators...

Фото профиля BubbieJ
BubbieJ1 год назад

I wonder what the new Minister of AI will do @EvanLSolomon ?

Фото профиля Tired crusty old surgeon
Tired crusty old surgeon1 год назад

Dave, in the olden days it might have not made a difference. As the clinician traipsed down to the radiology suite with coffee in hand. Now it depends on the clinician..to accept AI? assuming Rad on site. BTW, how do you sue AI? ..what a mess?

Фото профиля Xylonmoon))((
Xylonmoon))((1 год назад

The waiting period for radiology exams is lengthy, the population is aging, which only means we need more radiologists, and up-to-date radiography equipment in our hospitals!

Фото профиля Acton Island
Acton Island1 год назад

We need professional screening for MRI reqs. The MVA with WAD 1 7 years ago doesn't need a 4th MRI.

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Geoffrey Hinton explains the coyote test for medical AI: radiology is already over the cliff, it just has not looked down yet "If you work as a radiologist, you're like the coyote that's already over the edge of the cliff, but hasn't yet looked down" "People should stop training radiologists now. It's just completely obvious that within 5 years deep learning is going to do better than radiologists" "It might be 10 years, but we got plenty of radiologists already" "There's going to be thousands of applications of the deep learning technology we currently have, especially stuff using fast chips" "Thousands of applications and hundreds of them are going to work, and those are the ones you're going to hear about" Most healthcare AI conversations still price the bottleneck as adoption. Hinton was pointing at a different bottleneck: medicine has specialties where the machine gets more examples than any human can. The brutal part is the error bar. If it is 5 years, training pipelines are already late. If it is 10, hospitals still have to redesign the workflow before the models arrive. - Geoffrey Hinton, Nobel laureate and pioneer of deep learning, at Creative Destruction Lab
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Geoffrey Hinton explains the coyote test for medical AI: radiology is already over the cliff, it just has not looked down yet "If you work as a radiologist, you're like the coyote that's already over the edge of the cliff, but hasn't yet looked down" "People should stop training radiologists now. It's just completely obvious that within 5 years deep learning is going to do better than radiologists" "It might be 10 years, but we got plenty of radiologists already" "There's going to be thousands of applications of the deep learning technology we currently have, especially stuff using fast chips" "Thousands of applications and hundreds of them are going to work, and those are the ones you're going to hear about" Most healthcare AI conversations still price the bottleneck as adoption. Hinton was pointing at a different bottleneck: medicine has specialties where the machine gets more examples than any human can. The brutal part is the error bar. If it is 5 years, training pipelines are already late. If it is 10, hospitals still have to redesign the workflow before the models arrive. - Geoffrey Hinton, Nobel laureate and pioneer of deep learning, at Creative Destruction Lab

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55,948 просмотров • 26 дней назад

Jensen Huang says Geoffrey Hinton predicted 5 years ago that AI would replace radiologists and he’s right — but not in the way people expected. AI has swept the whole field. Today, just about every radiologist is using AI in some way. But here’s the twist: the number of radiologists actually grew. Why? Because their real purpose isn’t studying the image — it’s diagnosing disease. AI now studies images faster, more precisely, in 3D instead of 2D, without mistakes or fatigue. Meaning radiologists can diagnose more people than ever before. Hospitals get better economics → serve more patients → and hire more radiologists.
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