4 ms·

Emergency medicine is the coding of medicine. Fast feedback loop, requires broad rather than deep judgement, concrete next steps. The AI coding improvement sho

by sdwr 5mo ago

Emergency medicine is the coding of medicine. Fast feedback loop, requires broad rather than deep judgement, concrete next steps. The AI coding improvement should be partially transferrable to other disciplines without recreating the training environment that made it possible in the first place. The model itself has learned what correct solutions "feel like", and the training process and meta-knowledge must have improved a huge amount.

dghlsakjg 5mo ago
I would argue that the ED is the least similar to code. You have the most unknowns, unreliable data and history, non deterministic options and time constraints. An ER staff is frequently making inferences based on a variety of things like weather, what the pt is wearing, what smells are present, and a whole lot of other intangibles. Frequently the patients are just outright lying to the doctor. An AI will not pick up on any of that.
- TurdF3rguson 5mo ago
  > An AI will not pick up on any of that. It will if it trains on data like that. It's all about the training data.
  - mrbungie 5mo ago
    The user will be adversarial and probably learn new tricks to trick the machine, this is not solvable (only) via training data.
    - bonesss 5mo ago
      We have that expression “garbage in, garbage out. My sense is that doctors and AI would be doing a lot better if they were just doing medicine, not being a contact surface for failures of housing, mental health and addiction services, and social systems. Drug seeking and the rest should be non-issues, but drug seekers are informed and adaptive adversariesz
  - n8henrie 5mo ago
    Unfortunately the training data is absolute garbage. Diagnostic standards in (at least emergency, but I think other specialties) medicine are largely a joke -- ultimately it's often either autopsy or "expert consensus." We get to bill more for more serious diagnoses. The amount of patients I see with a "stroke" or "heart attack" diagnosis that clearly had no such thing is truly wild. We can be sued for tens of millions of dollars for missing a serious diagnosis, even if we know an alternative explanation is more likely. If AI is able to beat an average doctor, it will be due to alleviating perverse incentives. But I can't imagine where we could get training data that would let it be any less of a fountain of garbage than many doctors. Without a large amount of good training data, how could AI possibly be good at doctoring IRL?
    - TurdF3rguson 5mo ago
      You just get 1M doctors to wear body cams for a year. Now you have a model that has thousands of times your experience with patients, encyclopedic knowledge of every ailment including ones that never present in your geography, read all the latest papers, etc.. I don't understand how you think this doesn't win vs a human doctor.
      - xarope 5mo ago
        In healthcare, HIPAA/GDPR equivalent would block this. Let's be realistic in our discussion; this is not the same as google buying up a library worth of books, scanning and destroying them
        TurdF3rguson 5mo ago
        There are other countries, and the patients in them all have similar data
        notahacker 5mo ago
        Other countries actually don't necessarily have a similar mix of ailments, median patient appearance and style of communication or even recommended course of action and most of the ones with more sophisticated medical care also have strict medical privacy laws. If you're genuinely unaware of this, I'm not sure you're in a position to be making "one year with a camera, how hard can it be" arguments... (Where AI is likely to actually excel in medicine is parsing datasets that are much easier to do context free number crunching on than ER rooms, some of which physicians don't even have access to ...)
  - zbentley 5mo ago
    To give this more credit than it perhaps deserves: training aside, getting the situational data into the context is a more significant problem here. Pt's chart is complex/wrong? Gotta ingest that into context. Chart contains images/scanned and not OCR'd text? Gotta do an image recognition pass. Diagnosis needs to know what the pt's wearing (i.e. radiation badge)? Gotta do an image recognition pass. Diagnosis needs to know what the weather's like? Internet API access of some kind. Hope the WAN/API are all working! If they're not, do you fail open or closed? Patient might be lying? Gotta do video/audio analysis to assess that likelihood--oh, and train a model that fully solves one of the holy grails of computer vision/audio analysis reliably and with a super low false-positive rate before you do. And if it guesses wrong, enjoy the incredibly easy-to-prosecute lawsuit. Patient might be lying, but the biggest clue is e.g. smell of alcohol on their breath? Now you need some sort of olfactory sensor kit and training for it--a lot more than just "low quality body cam and a mic". Patient's ODing on a street drug that became abundant in the last few months? Gotta somehow learn about recent local medical/police history that post-dates the training set, or else you might be pouring gas on a fire if you give them Narcan. And that's assuming you know enough to search for information about that drug, and that they didn't lie to you about what they took. Addicts never do that. Failures in each of those systems bring down the chance of an effective diagnosis, so they need a fairly obsessive amount of model introspection/thinking/double-checking, and humans on standby as a fallback if the AI's less than confident (assuming that LLMs can be given a sense of a confidence level in the future, versus the current state of the art of "text-predict a guess about what your confidence level might be"). Put that all together, and even with the AI compute speed available years from now and a perfectly trained futuristic model that's preternaturally good at this stuff, I'm not sure that that the reliability and, more importantly, the turnaround time of that diagnostic pass is going to be any good compared to a human ER doc.

by sdwr 5mo ago

dghlsakjg 5mo ago

I would argue that the ED is the least similar to code. You have the most unknowns, unreliable data and history, non deterministic options and time constraints. An ER staff is frequently making inferences based on a variety of things like weather, what the pt is wearing, what smells are present, and a whole lot of other intangibles. Frequently the patients are just outright lying to the doctor. An AI will not pick up on any of that.

TurdF3rguson 5mo ago
> An AI will not pick up on any of that. It will if it trains on data like that. It's all about the training data.
- mrbungie 5mo ago
  The user will be adversarial and probably learn new tricks to trick the machine, this is not solvable (only) via training data.
  - bonesss 5mo ago
    We have that expression “garbage in, garbage out. My sense is that doctors and AI would be doing a lot better if they were just doing medicine, not being a contact surface for failures of housing, mental health and addiction services, and social systems. Drug seeking and the rest should be non-issues, but drug seekers are informed and adaptive adversariesz
- n8henrie 5mo ago
  Unfortunately the training data is absolute garbage. Diagnostic standards in (at least emergency, but I think other specialties) medicine are largely a joke -- ultimately it's often either autopsy or "expert consensus." We get to bill more for more serious diagnoses. The amount of patients I see with a "stroke" or "heart attack" diagnosis that clearly had no such thing is truly wild. We can be sued for tens of millions of dollars for missing a serious diagnosis, even if we know an alternative explanation is more likely. If AI is able to beat an average doctor, it will be due to alleviating perverse incentives. But I can't imagine where we could get training data that would let it be any less of a fountain of garbage than many doctors. Without a large amount of good training data, how could AI possibly be good at doctoring IRL?
  - TurdF3rguson 5mo ago
    You just get 1M doctors to wear body cams for a year. Now you have a model that has thousands of times your experience with patients, encyclopedic knowledge of every ailment including ones that never present in your geography, read all the latest papers, etc.. I don't understand how you think this doesn't win vs a human doctor.
    - xarope 5mo ago
      In healthcare, HIPAA/GDPR equivalent would block this. Let's be realistic in our discussion; this is not the same as google buying up a library worth of books, scanning and destroying them
      - TurdF3rguson 5mo ago
        There are other countries, and the patients in them all have similar data
        notahacker 5mo ago
        Other countries actually don't necessarily have a similar mix of ailments, median patient appearance and style of communication or even recommended course of action and most of the ones with more sophisticated medical care also have strict medical privacy laws. If you're genuinely unaware of this, I'm not sure you're in a position to be making "one year with a camera, how hard can it be" arguments... (Where AI is likely to actually excel in medicine is parsing datasets that are much easier to do context free number crunching on than ER rooms, some of which physicians don't even have access to ...)
- zbentley 5mo ago
  To give this more credit than it perhaps deserves: training aside, getting the situational data into the context is a more significant problem here. Pt's chart is complex/wrong? Gotta ingest that into context. Chart contains images/scanned and not OCR'd text? Gotta do an image recognition pass. Diagnosis needs to know what the pt's wearing (i.e. radiation badge)? Gotta do an image recognition pass. Diagnosis needs to know what the weather's like? Internet API access of some kind. Hope the WAN/API are all working! If they're not, do you fail open or closed? Patient might be lying? Gotta do video/audio analysis to assess that likelihood--oh, and train a model that fully solves one of the holy grails of computer vision/audio analysis reliably and with a super low false-positive rate before you do. And if it guesses wrong, enjoy the incredibly easy-to-prosecute lawsuit. Patient might be lying, but the biggest clue is e.g. smell of alcohol on their breath? Now you need some sort of olfactory sensor kit and training for it--a lot more than just "low quality body cam and a mic". Patient's ODing on a street drug that became abundant in the last few months? Gotta somehow learn about recent local medical/police history that post-dates the training set, or else you might be pouring gas on a fire if you give them Narcan. And that's assuming you know enough to search for information about that drug, and that they didn't lie to you about what they took. Addicts never do that. Failures in each of those systems bring down the chance of an effective diagnosis, so they need a fairly obsessive amount of model introspection/thinking/double-checking, and humans on standby as a fallback if the AI's less than confident (assuming that LLMs can be given a sense of a confidence level in the future, versus the current state of the art of "text-predict a guess about what your confidence level might be"). Put that all together, and even with the AI compute speed available years from now and a perfectly trained futuristic model that's preternaturally good at this stuff, I'm not sure that that the reliability and, more importantly, the turnaround time of that diagnostic pass is going to be any good compared to a human ER doc.