8 ms·

Pathologists as a specialty has been grousing about this for several years, at least since 2021 when the College of American Pathologists established the AI Com

Pathologists as a specialty has been grousing about this for several years, at least since 2021 when the College of American Pathologists established the AI Committee. As a trivial example: any trained model deployed will necessarily be behind any new classification of tumors. This makes it harder to push the science and clinical diagnosis of cancer forward. The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. It's absolutely fascinating watching software developers come to grips with what they have wrought.

Schiendelman 2y ago
The healthcare diagnosis one may be wrong. For existing known diagnoses, (or at least the sliver of diagnoses in this one study), AI can beat doctors - and doctors don't like listening when it challenges them, so it will disrupt them badly as people learn they can provide data from tests directly to AI agents. Sure, this doesn't replace new diagnoses, but the vaaaast majority of failures to diagnose are for existing well classified diagnoses. https://www.advisory.com/daily-briefing/2024/12/03/ai-diagnosis-ec https://www.advisory.com/daily-briefing/2024/12/03/ai-diagno... Edit: yeah, people don't like this.
- killjoywashere 2y ago
  I'm familiar with the linked study, which presents legitimately challenging analytic problems. There's a difference between challenging analytic problems and new analytic problems. A new platform poses new analytic problems. A new edition of the WHO's classification of skin tumors (1), for example, presents new analytic problems. (1) https://tumourclassification.iarc.who.int/chapters/64 https://tumourclassification.iarc.who.int/chapters/64
  - Schiendelman 2y ago
    Right, but the vast majority of patient issues today are missing existing diagnoses, not new ones.
- lab14 2y ago
  I think OP was referring to the case where new illnesses that are not part of the training set are never going to be diagnosed by AI.
  - onion2k 2y ago
    It's only a problem if hospitals replace doctors with AI. If they employ AI as well then outcomes will improve. Using AI to find the ones AI can identify means doctors have more time to focus on the ones that AI can't find. Of course, that's not what's going to happen. :/
    - cbg0 2y ago
      > Using AI to find the ones AI can identify means doctors have more time to focus on the ones that AI can't find. That's not how that would work in the real world. In a lot of places a doctor has to put their signature or stamp on a medical document, making them liable for what is on that paper. Just because the AI can do it, that doesn't mean the doctor won't have to double check it, which negates the time saved. I would wager AI-assisted would be more helpful to reduce things doctors might miss instead of partially or completely replacing them.
      - killjoywashere 2y ago
        Interesting. Do you see any versions of the future where use of AI could actually make the physician take more time?
        cbg0 2y ago
        Let's assume you program it so that if it believes with 95% certainty that a patient has a certain condition it will present it to the doctor. While the doctor doesn't agree with it, the whole process between doctor-patient-hospital-insurer might be automated to the point where it's simpler to put the patient through the motions of getting additional checks than the doctor fighting the wrong diagnosis, thus the doctor will have to spend more time to follow up on confirming that this condition is not really present. I don't have a crystal ball, so this is a made-up scenario.
- resource_waste 2y ago
  It has been interesting to see the excuses from doctors, why we need error prone humans instead of higher quality robots. >Empathy (lol... from doctors?) >New undetectable cases (lol... AI doesnt have to wait 1 year for an optional continuing education class. I had doctors a few years ago recommending a dangerous expensive surgery over a safer cheaper laser procedure) >corruptible (lmaooo) We humans are empathetic to the thought our 'friendly' doctor might be unemployed. However, we shouldn't let that cause negative health outcomes because we were being 'nice'.
  - 52-6F-62 2y ago
    So… we put all of our trust (wait, at that point it might be called faith) into this machine… If it ever turns on us, begins to malfunction in unforeseen ways, or goes away completely—then what? Shortsighted, all of it.
kaonwarb 2y ago
I doubt it is easier to retrain a large, dispersed group of humans on a new classification of tumors than it is to retrain a model on the same.
- darkerside 2y ago
  Well, the difference is that people eventually die or retired so they are constantly being replaced
- Vegenoid 2y ago
  I think it depends on what you mean by “easier”. Dispersing knowledge through people is more intuitive, and tends to happen organically.
- killjoywashere 2y ago
  Not if they're trained to work through the problem each time they encounter it and stay up with their clinical training. The day the new classification drops many have already heard about it. You also assume that all the models in use will in fact be retrained. Generally, this position flies in the face of lived experience. AI is in fact stifling adoption of new things across many industries.
  - kaonwarb 2y ago
    My position is informed by my own experience; I am not a physician, but have worked closely with a large number of them in a healthcare-oriented career. I've repeatedly noted long-term resistance of many physicians to updating their priors based on robust new evidence. There are definitely many physicians who do take in the latest developments judiciously. But I find the long tail of default resistance to be very, very long.
    - shermantanktop 2y ago
      I was just explaining to a UK colleague about how the American health care system makes getting treatment (and getting it paid for) into a DIY project. And so as a medical shopper, if I’m getting a very standard established treatment I might go for the older experienced doctor, but if it’s a new thing I’d opt for someone more recently graduated. I’m sure the same thing applies worldwide.
fsndz 2y ago
I think a one year gap in adoption of new tech is not that bad. Isn't it better to always go for the mature tech first ? The real change will come from the fact that because of AI, compute will be so cheap in the coming years: https://medium.com/thoughts-on-machine-learning/a-future-of-cheap-compute-7be643af7923?sk=b5fd602d8e206cdfc128654980b94a92 https://medium.com/thoughts-on-machine-learning/a-future-of-...
- deleted 2y ago
  [deleted]
- layer8 2y ago
  This is assuming that new technology will grow the same as in pre-LLM times, and merely be picked up a year late. But use of LLMs is likely to cause new developments to grow and spread slower, because of the reduced visibility. It may take much longer for a new development to gain currency to the extent that it becomes sufficiently visible in the training data. This also slows competition between evolving technologies. In addition, as the article describes, the LLM services have biases built in to them even among existing technologies. It amplifies existing preferences, leading to less diversity and competition between technologies. Tech leads will have to weigh between the qualities of a technology on its own merits against how well it is supported by an LLM.
raincole 2y ago
I know nothing about pathology, but in terms of software, I think slower adoption to new tech is what we need, especially when the "new tech" is just a 5% faster javascript framework. By the way, for content creation, the only platfrom that really favors new creators is TikTok. Whether it leads to higher content quality is left for one's judgement.
- hartator 2y ago
  a 5% _slower_ javascript framework
  - ben_w 2y ago
    Surely 50% slower, compounding each year? Jokes aside, I find it curious what does and doesn't gain traction in tech. The slowness of the IPv6 was already an embarrassment when I learned about it in university… 21 years ago, and therefore before the people currently learning about it in university had been conceived. What actually took hold? A growing pantheon of software architecture styles and patterns, and enough layers of abstraction to make jokes about Java class names from 2011 (and earlier) seem tame in comparison: https://news.ycombinator.com/item?id=3215736 https://news.ycombinator.com/item?id=3215736 The way all of us seem to approach code, the certainty of what the best way to write it looks like, the degree to which a lone developer can still build fantastic products and keep up with an entire team… we're less like engineers, more like poets arguing over a preferred form and structure of the words, of which metaphors and simile work best — and all the while, the audience is asking us to rhyme "orange" or "purple"
    - saulpw 2y ago
      The slowness to adopt IPv6 is because it's not a great design. Going from 32-bits to 128-bits is complete overengineering. We will never need 128-bits of network address space as long as we are confined to this solar system, and the resulting addresses are extremely cumbersome to use. (Can you tell someone your IPv6 address over the phone? Can you see it on one monitor and type it into a different computer? Can you remember it for the 10 seconds it takes to walk over to the other terminal?) 48-bit addresses would have been sufficient, and at worst they could have gone with 64-bit addresses. This is already too cumbersome (9-12 base36 digits), but maybe with area-code like segmentation it could be rotated into manageable. 128-bits is just not workable.
deleted 2y ago
[deleted]
resource_waste 2y ago
>Pathologists as a specialty has been grousing about this for several years, at least since 2021 when the College of American Pathologists established the AI Committee. This sounds like Moral Coating for what is otherwise protection of the Status Quo. High paid doctors do not want to be replaced by AI. They will use every excuse to keep their high paying job.
diggan 2y ago
> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. Compared to what though? Compared to Limeware/Kaazaa back in the day, or compared to buying records in a store? Personally, I find it easier than ever to find brand new music, mostly because Spotify still surfaces new things with ease for me (and always have been, since I started using it in 2008), and platforms like Bandcamp makes it trivial to find new artists that basically started uploading music yesterday.
- gosub100 2y ago
  Or compared to the days of radio, having labels decide what's on the mainstream and the indie college stations doing the unpaid work (and giving listeners the gift) of discovering "lost" hits.
- Blackthorn 2y ago
  Compared to Myspace. The difference for anyone who lived through it is night and day.
- serviceberry 2y ago
  > Compared to what though? Compared to Limeware/Kaazaa back in the day, or compared to buying records in a store? Compared to curation by other humans. Be it music labels, magazines, radio DJs, or a person sharing their playlist or giving you a mixtape. In this model, tastes never overlap perfectly, so you're exposed to unfamiliar music fairly regularly, often in some emotional context that makes you more likely to accept something new. Algorithms don't really do that. They could, but no one is designing them that way. If I listen predominantly to female vocalists on Spotify for a week, I'm only getting female vocalists from now on.
  - esafak 2y ago
    I don't get that. Its recommender has been great for me. And there are lots of playlist if I want to try something completely different.
golergka 2y ago
> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. I can understand other issues, but this has nothing to do with that. Models don't have to be re-trained to recommend new music. That's not how recommendation systems work.
hindsightbias 2y ago
> new music I keep thinking I'm going crazy until Rick Beato explains that yes, I am just an RNN Meat Popsicle and the world is interpolated: https://www.youtube.com/watch?v=j_9Larw-hJM https://www.youtube.com/watch?v=j_9Larw-hJM
idunnoman1222 2y ago
This is the fault of the regulators. There’s no reason that new discoveries are not put in a queue to train a new AI and when there are enough to make it worth the run, you do the run and then you give the doctors old model and new model and they run both and compare the results.
meroes 2y ago
> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. Why does music continually entrench the older stuff (will we ever stop playing classic rock bands) whereas video streaming platforms like Netflix and YouTube try to hide/get rid of the old stuff?
- adovenmuehle 2y ago
  I wonder if shows like The Office, Parks and Rec, Seinfeld, etc end up becoming the "classic rock" of streaming.
- AlienRobot 2y ago
  The main issue with AI, and ironically the reason why ChatGPT is the best one, is whom it works for. AI doesn't work for the user. It couldn't care less if the user is happy or not. AI is designed first and foremost to make more money for the company. Its metrics are increased engagement and time on site, more sales, sales with better margins. Consequently, the user often has no choice or control over what the AI recommends for them. The AI is recommending what makes more sense for the company, so the user input is unnecessary. Think of AI not as your assistant, but as a salesman. One interesting consequence of this situation I found was that Youtube published a video "explaining" to creators why their videos don't have reach in the algorithm, where they essentially said a bunch of nothing. They throw some data at the AI, and the AI figures it out. Most importantly, they disclosed that one of the key metrics driving their algorithm is "happiness" or "satisfaction" partially gathered through surveys, which (although they didn't explicitly say this) isn't a metric that they provide creators with, thus it's possible for Youtube to optimize for this metric, but not for creators to optimize for it. That's because the AI works for Youtube. It doesn't work for creators, just as it doesn't work for users. People are complex creatures, so any attempt at guessing what someone wants at a specific time without any input from them seems just flawed at a conceptual level. If Youtube wanted to help users, they would just fix their search, or incorporate AI in the search box. That's a place where LLMs could work, I think. When you look at things this way, the reason why Netflix/Youtube get rid of old stuff has nothing to do with users, but with some business strategy that they have that differs from the music industry.

Schiendelman 2y ago

The healthcare diagnosis one may be wrong. For existing known diagnoses, (or at least the sliver of diagnoses in this one study), AI can beat doctors - and doctors don't like listening when it challenges them, so it will disrupt them badly as people learn they can provide data from tests directly to AI agents. Sure, this doesn't replace new diagnoses, but the vaaaast majority of failures to diagnose are for existing well classified diagnoses. https://www.advisory.com/daily-briefing/2024/12/03/ai-diagnosis-ec https://www.advisory.com/daily-briefing/2024/12/03/ai-diagno... Edit: yeah, people don't like this.

killjoywashere 2y ago
I'm familiar with the linked study, which presents legitimately challenging analytic problems. There's a difference between challenging analytic problems and new analytic problems. A new platform poses new analytic problems. A new edition of the WHO's classification of skin tumors (1), for example, presents new analytic problems. (1) https://tumourclassification.iarc.who.int/chapters/64 https://tumourclassification.iarc.who.int/chapters/64
- Schiendelman 2y ago
  Right, but the vast majority of patient issues today are missing existing diagnoses, not new ones.
lab14 2y ago
I think OP was referring to the case where new illnesses that are not part of the training set are never going to be diagnosed by AI.
- onion2k 2y ago
  It's only a problem if hospitals replace doctors with AI. If they employ AI as well then outcomes will improve. Using AI to find the ones AI can identify means doctors have more time to focus on the ones that AI can't find. Of course, that's not what's going to happen. :/
  - cbg0 2y ago
    > Using AI to find the ones AI can identify means doctors have more time to focus on the ones that AI can't find. That's not how that would work in the real world. In a lot of places a doctor has to put their signature or stamp on a medical document, making them liable for what is on that paper. Just because the AI can do it, that doesn't mean the doctor won't have to double check it, which negates the time saved. I would wager AI-assisted would be more helpful to reduce things doctors might miss instead of partially or completely replacing them.
    - killjoywashere 2y ago
      Interesting. Do you see any versions of the future where use of AI could actually make the physician take more time?
      - cbg0 2y ago
        Let's assume you program it so that if it believes with 95% certainty that a patient has a certain condition it will present it to the doctor. While the doctor doesn't agree with it, the whole process between doctor-patient-hospital-insurer might be automated to the point where it's simpler to put the patient through the motions of getting additional checks than the doctor fighting the wrong diagnosis, thus the doctor will have to spend more time to follow up on confirming that this condition is not really present. I don't have a crystal ball, so this is a made-up scenario.
resource_waste 2y ago
It has been interesting to see the excuses from doctors, why we need error prone humans instead of higher quality robots. >Empathy (lol... from doctors?) >New undetectable cases (lol... AI doesnt have to wait 1 year for an optional continuing education class. I had doctors a few years ago recommending a dangerous expensive surgery over a safer cheaper laser procedure) >corruptible (lmaooo) We humans are empathetic to the thought our 'friendly' doctor might be unemployed. However, we shouldn't let that cause negative health outcomes because we were being 'nice'.
- 52-6F-62 2y ago
  So… we put all of our trust (wait, at that point it might be called faith) into this machine… If it ever turns on us, begins to malfunction in unforeseen ways, or goes away completely—then what? Shortsighted, all of it.

kaonwarb 2y ago

I doubt it is easier to retrain a large, dispersed group of humans on a new classification of tumors than it is to retrain a model on the same.

darkerside 2y ago
Well, the difference is that people eventually die or retired so they are constantly being replaced
Vegenoid 2y ago
I think it depends on what you mean by “easier”. Dispersing knowledge through people is more intuitive, and tends to happen organically.
killjoywashere 2y ago
Not if they're trained to work through the problem each time they encounter it and stay up with their clinical training. The day the new classification drops many have already heard about it. You also assume that all the models in use will in fact be retrained. Generally, this position flies in the face of lived experience. AI is in fact stifling adoption of new things across many industries.
- kaonwarb 2y ago
  My position is informed by my own experience; I am not a physician, but have worked closely with a large number of them in a healthcare-oriented career. I've repeatedly noted long-term resistance of many physicians to updating their priors based on robust new evidence. There are definitely many physicians who do take in the latest developments judiciously. But I find the long tail of default resistance to be very, very long.
  - shermantanktop 2y ago
    I was just explaining to a UK colleague about how the American health care system makes getting treatment (and getting it paid for) into a DIY project. And so as a medical shopper, if I’m getting a very standard established treatment I might go for the older experienced doctor, but if it’s a new thing I’d opt for someone more recently graduated. I’m sure the same thing applies worldwide.

fsndz 2y ago

I think a one year gap in adoption of new tech is not that bad. Isn't it better to always go for the mature tech first ? The real change will come from the fact that because of AI, compute will be so cheap in the coming years: https://medium.com/thoughts-on-machine-learning/a-future-of-cheap-compute-7be643af7923?sk=b5fd602d8e206cdfc128654980b94a92 https://medium.com/thoughts-on-machine-learning/a-future-of-...

deleted 2y ago
[deleted]
layer8 2y ago
This is assuming that new technology will grow the same as in pre-LLM times, and merely be picked up a year late. But use of LLMs is likely to cause new developments to grow and spread slower, because of the reduced visibility. It may take much longer for a new development to gain currency to the extent that it becomes sufficiently visible in the training data. This also slows competition between evolving technologies. In addition, as the article describes, the LLM services have biases built in to them even among existing technologies. It amplifies existing preferences, leading to less diversity and competition between technologies. Tech leads will have to weigh between the qualities of a technology on its own merits against how well it is supported by an LLM.

raincole 2y ago

I know nothing about pathology, but in terms of software, I think slower adoption to new tech is what we need, especially when the "new tech" is just a 5% faster javascript framework. By the way, for content creation, the only platfrom that really favors new creators is TikTok. Whether it leads to higher content quality is left for one's judgement.

hartator 2y ago
a 5% _slower_ javascript framework
- ben_w 2y ago
  Surely 50% slower, compounding each year? Jokes aside, I find it curious what does and doesn't gain traction in tech. The slowness of the IPv6 was already an embarrassment when I learned about it in university… 21 years ago, and therefore before the people currently learning about it in university had been conceived. What actually took hold? A growing pantheon of software architecture styles and patterns, and enough layers of abstraction to make jokes about Java class names from 2011 (and earlier) seem tame in comparison: https://news.ycombinator.com/item?id=3215736 https://news.ycombinator.com/item?id=3215736 The way all of us seem to approach code, the certainty of what the best way to write it looks like, the degree to which a lone developer can still build fantastic products and keep up with an entire team… we're less like engineers, more like poets arguing over a preferred form and structure of the words, of which metaphors and simile work best — and all the while, the audience is asking us to rhyme "orange" or "purple"
  - saulpw 2y ago
    The slowness to adopt IPv6 is because it's not a great design. Going from 32-bits to 128-bits is complete overengineering. We will never need 128-bits of network address space as long as we are confined to this solar system, and the resulting addresses are extremely cumbersome to use. (Can you tell someone your IPv6 address over the phone? Can you see it on one monitor and type it into a different computer? Can you remember it for the 10 seconds it takes to walk over to the other terminal?) 48-bit addresses would have been sufficient, and at worst they could have gone with 64-bit addresses. This is already too cumbersome (9-12 base36 digits), but maybe with area-code like segmentation it could be rotated into manageable. 128-bits is just not workable.

deleted 2y ago

[deleted]

resource_waste 2y ago

>Pathologists as a specialty has been grousing about this for several years, at least since 2021 when the College of American Pathologists established the AI Committee. This sounds like Moral Coating for what is otherwise protection of the Status Quo. High paid doctors do not want to be replaced by AI. They will use every excuse to keep their high paying job.

diggan 2y ago

> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. Compared to what though? Compared to Limeware/Kaazaa back in the day, or compared to buying records in a store? Personally, I find it easier than ever to find brand new music, mostly because Spotify still surfaces new things with ease for me (and always have been, since I started using it in 2008), and platforms like Bandcamp makes it trivial to find new artists that basically started uploading music yesterday.

gosub100 2y ago
Or compared to the days of radio, having labels decide what's on the mainstream and the indie college stations doing the unpaid work (and giving listeners the gift) of discovering "lost" hits.
Blackthorn 2y ago
Compared to Myspace. The difference for anyone who lived through it is night and day.
serviceberry 2y ago
> Compared to what though? Compared to Limeware/Kaazaa back in the day, or compared to buying records in a store? Compared to curation by other humans. Be it music labels, magazines, radio DJs, or a person sharing their playlist or giving you a mixtape. In this model, tastes never overlap perfectly, so you're exposed to unfamiliar music fairly regularly, often in some emotional context that makes you more likely to accept something new. Algorithms don't really do that. They could, but no one is designing them that way. If I listen predominantly to female vocalists on Spotify for a week, I'm only getting female vocalists from now on.
- esafak 2y ago
  I don't get that. Its recommender has been great for me. And there are lots of playlist if I want to try something completely different.

golergka 2y ago

> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. I can understand other issues, but this has nothing to do with that. Models don't have to be re-trained to recommend new music. That's not how recommendation systems work.

hindsightbias 2y ago

> new music I keep thinking I'm going crazy until Rick Beato explains that yes, I am just an RNN Meat Popsicle and the world is interpolated: https://www.youtube.com/watch?v=j_9Larw-hJM https://www.youtube.com/watch?v=j_9Larw-hJM

idunnoman1222 2y ago

This is the fault of the regulators. There’s no reason that new discoveries are not put in a queue to train a new AI and when there are enough to make it worth the run, you do the run and then you give the doctors old model and new model and they run both and compare the results.

meroes 2y ago

> The entire music community has been complaining about how old music gets more recommendations on streaming platforms, necessarily making it harder for new music to break out. Why does music continually entrench the older stuff (will we ever stop playing classic rock bands) whereas video streaming platforms like Netflix and YouTube try to hide/get rid of the old stuff?

adovenmuehle 2y ago
I wonder if shows like The Office, Parks and Rec, Seinfeld, etc end up becoming the "classic rock" of streaming.
AlienRobot 2y ago
The main issue with AI, and ironically the reason why ChatGPT is the best one, is whom it works for. AI doesn't work for the user. It couldn't care less if the user is happy or not. AI is designed first and foremost to make more money for the company. Its metrics are increased engagement and time on site, more sales, sales with better margins. Consequently, the user often has no choice or control over what the AI recommends for them. The AI is recommending what makes more sense for the company, so the user input is unnecessary. Think of AI not as your assistant, but as a salesman. One interesting consequence of this situation I found was that Youtube published a video "explaining" to creators why their videos don't have reach in the algorithm, where they essentially said a bunch of nothing. They throw some data at the AI, and the AI figures it out. Most importantly, they disclosed that one of the key metrics driving their algorithm is "happiness" or "satisfaction" partially gathered through surveys, which (although they didn't explicitly say this) isn't a metric that they provide creators with, thus it's possible for Youtube to optimize for this metric, but not for creators to optimize for it. That's because the AI works for Youtube. It doesn't work for creators, just as it doesn't work for users. People are complex creatures, so any attempt at guessing what someone wants at a specific time without any input from them seems just flawed at a conceptual level. If Youtube wanted to help users, they would just fix their search, or incorporate AI in the search box. That's a place where LLMs could work, I think. When you look at things this way, the reason why Netflix/Youtube get rid of old stuff has nothing to do with users, but with some business strategy that they have that differs from the music industry.