14 ms·

As someone who works in healthcare, so much of what I read about AI makes me think that the people who are enthusiastic about healthcare AI don't have much expe

by dm319 4y ago

As someone who works in healthcare, so much of what I read about AI makes me think that the people who are enthusiastic about healthcare AI don't have much experience doing it. The scenarios rarely seem to fit with what I'm actually practicing. Most of medicine is boring, it is largely routine, and if we don't know what's going on, it's because we're not the right person to be managing the patient. Most of my time is spent talking to people - patients, colleagues, family. I explain the diagnosis, I talk about the plan, I am getting ideas of what the patient wants and values, and then actioning it. I spend very little of my time like Dr House pondering what the next most important test to perform is for a patient who is confounding us.

ericmcer 4y ago
That scenario sounds like it lends itself more to AI automation than a Dr. House type one.
- kbenson 4y ago
  I don't know, compassion and understanding and nuanced understanding of individual desires when talking to someone is not what I associate AI with in my mind, but being able to assess sociological and cultural taboos and try to what a patient actually wants rather then what they might initially express seems like something I good doctor would get to through explorative conversation.
  - junipertea 4y ago
    Maybe removing a human from the equation would lead to more honest outcome? E.g. people google all sorts of issues more earnestly than they would describe it to the doctors. The bottleneck would be properly understanding what the user intends, which might be out of reach.
    - ben_w 4y ago
      Indeed. Language has been historically difficult for AI, but I think it's even tougher here — language is less and less reliable the further we get from a shared experience, and this is a problem when describing our experiences of our own bodies, and much worse when describing our own minds. For example, when I was coming off an SSRI, I was forewarned that I might get a sensation of "electric shocks"; the actual experience wasn't like that, though I could tell why they chose to describe it like that. How different is the tightness in the chest during a heart attack from the tightness in the chest from exercising chest muscles? I have no idea how doctors, GPs, and nurses manage this, though they seem to have relatively little trouble.
  - dm319 4y ago
    My experience of chatting with an internet chat-bot when trying to get some help with a product gives me little confidence we are close here. Edit: wording
deleted 4y ago
[deleted]
lostlogin 4y ago
I work in radiology with MRI as a tech. We use AI slightly differently to the examples here, but it’s changing a lot of what we do. It’s more about enhancing images than directly about diagnosing. The image is denoised ‘intelligently’ in k-space and then the resolution is doubled via another AI process in the image domain (or maybe the resolution is quadrupled, as it depends on how you measure it. Our pixel count doubles in x and y dimensions). These are 2 distinct processes which we can turn on or off and have some parameters which with we can alter the process. The result is amazing and image quality has gone up a lot. We haven’t got a full grasp yet and have a few theories. The vendors are also still getting to grips. We think the training data set turns out to have some weird influences on requires acquisition parameters. For example, parallel imaging factor 4 works well, 3 and 2 less so, which is not intuitive. More acceleration being better for image quality is not how MRI used to work (except in a few edge cases). Bandwidth, averages, square pixel, turbo factor and appropriate TE matter a bit more than they did pre-AI. Images are now acquired faster, look better and sequence selection can be better tailored to the patient as we have less of a time pressure. I’d put our images up against almost anything I’ve seen before as examples of good work. We are seeing anatomy and pathology that we didn’t previously appreciate. Sceptics ask if the things we see are really there, but after some time with the images the concern goes away and the pre-AI images just look broken. In the below link, ignore Gain (it isn’t that great), Boost and Sharp are the vendor names for the good stuff. The brochure undersells it. https://www.siemens-healthineers.com/magnetic-resonance-imaging/technologies-and-innovations/deep-resolve https://www.siemens-healthineers.com/magnetic-resonance-imag...
- ggm 4y ago
  My partner had a clinician review her paperwork and say "why are you here" explaining the enhanced imaging was leading to tentative concerns being raised about structural change so small it was below the threshold for safe surgical treatment. Moral of the story: the imaging has got so good that diagnostics is now on the fringe of over diagnosing and the stats need to catch up
  - lostlogin 4y ago
    This has been a thing for a long time, with MRI in particular. It gets quite philosophical. To diagnose something you need some pattern on the images. As resolution and tissue contrast improves you see more things, and the radiologist gets to decide if the appearance is something. When a clinician says there is a problem in some area of anatomy and there is something on the scan, the radiologist has to make a call. The great thing about being a tech is that making the call isn’t my job. I have noticed that keeping the field of view small tends to make me more friends. A half imaged liver haemangioma, a thyroid nodule or a white matter brain lesion as an incidental finding are a daily occurrence at least.
deleted 4y ago
[deleted]
esel2k 4y ago
So much this. I just interviewed about 10 doctors in the space of neurology and radiology to start some new projects. The truth is most of the headaches are from insurance coverage check or for radiologist for filling out correct reports. The fancy AI stuff is with maybe a few exceptions due to the great advancement imaging still far away from validation and I didn’t even start about it’s usage and gotomarket. Most of the cases the doctors sees are boring / regular cases - and problems like access to medical history is way more basic but more prevalent.
- the_d3f4ult 4y ago
  I'm a physician and former engineer and I agree with you. Even the giant tech companies that are in this space seem to have very little insight into how to utilize AI to improve healthcare. The mistake everyone seems to make is thinking that making a diagnosis is what makes medicine hard. It's not. e.g. An algorithm that can diagnose diabetic retinopathy from fundus photos 'as well as a specialist' makes for a good headline, but isn't particularly useful. Medicine is nothing like "House" in real life. AI should be leveraged to either improve efficiency or develop novel tools.
  - naijaboiler 4y ago
    I can't upvote this enough
- buscoquadnary 4y ago
  > and problems like access to medical history is way more basic but more prevalent. This here is a problem I wish we could solve, get a central data broker that could hold aggregate medical history and just dole it out to companies. My wife has to get Medical supplies on a regular basis, the company we got them from got bought out by a PEC so we wanted to try switching companies. Months later we still can't get all the Drs insurance and supplier lined up. The problem is of course as a security guy I know a centralized clearinghouse would be a nightmare from every stand point. The real solution may be in the development of a good protocol, but good luck getting the Med industry that is still putzing around on ASM/400s to adopt something like that. Even with the force of law, i.e. HIPPA
dan_mctree 4y ago
But doctors tend to be pretty good at that part of the job you're describing. It's the Dr House part that often seems lacking, difficult patients often spend years going to different doctors only to keep hearing useless suggestions. It'd be amazing if we could have a solution for those people and AI might be able to help

by dm319 4y ago

ericmcer 4y ago

That scenario sounds like it lends itself more to AI automation than a Dr. House type one.

kbenson 4y ago
I don't know, compassion and understanding and nuanced understanding of individual desires when talking to someone is not what I associate AI with in my mind, but being able to assess sociological and cultural taboos and try to what a patient actually wants rather then what they might initially express seems like something I good doctor would get to through explorative conversation.
- junipertea 4y ago
  Maybe removing a human from the equation would lead to more honest outcome? E.g. people google all sorts of issues more earnestly than they would describe it to the doctors. The bottleneck would be properly understanding what the user intends, which might be out of reach.
  - ben_w 4y ago
    Indeed. Language has been historically difficult for AI, but I think it's even tougher here — language is less and less reliable the further we get from a shared experience, and this is a problem when describing our experiences of our own bodies, and much worse when describing our own minds. For example, when I was coming off an SSRI, I was forewarned that I might get a sensation of "electric shocks"; the actual experience wasn't like that, though I could tell why they chose to describe it like that. How different is the tightness in the chest during a heart attack from the tightness in the chest from exercising chest muscles? I have no idea how doctors, GPs, and nurses manage this, though they seem to have relatively little trouble.
- dm319 4y ago
  My experience of chatting with an internet chat-bot when trying to get some help with a product gives me little confidence we are close here. Edit: wording

deleted 4y ago

[deleted]

lostlogin 4y ago

I work in radiology with MRI as a tech. We use AI slightly differently to the examples here, but it’s changing a lot of what we do. It’s more about enhancing images than directly about diagnosing. The image is denoised ‘intelligently’ in k-space and then the resolution is doubled via another AI process in the image domain (or maybe the resolution is quadrupled, as it depends on how you measure it. Our pixel count doubles in x and y dimensions). These are 2 distinct processes which we can turn on or off and have some parameters which with we can alter the process. The result is amazing and image quality has gone up a lot. We haven’t got a full grasp yet and have a few theories. The vendors are also still getting to grips. We think the training data set turns out to have some weird influences on requires acquisition parameters. For example, parallel imaging factor 4 works well, 3 and 2 less so, which is not intuitive. More acceleration being better for image quality is not how MRI used to work (except in a few edge cases). Bandwidth, averages, square pixel, turbo factor and appropriate TE matter a bit more than they did pre-AI. Images are now acquired faster, look better and sequence selection can be better tailored to the patient as we have less of a time pressure. I’d put our images up against almost anything I’ve seen before as examples of good work. We are seeing anatomy and pathology that we didn’t previously appreciate. Sceptics ask if the things we see are really there, but after some time with the images the concern goes away and the pre-AI images just look broken. In the below link, ignore Gain (it isn’t that great), Boost and Sharp are the vendor names for the good stuff. The brochure undersells it. https://www.siemens-healthineers.com/magnetic-resonance-imaging/technologies-and-innovations/deep-resolve https://www.siemens-healthineers.com/magnetic-resonance-imag...

ggm 4y ago
My partner had a clinician review her paperwork and say "why are you here" explaining the enhanced imaging was leading to tentative concerns being raised about structural change so small it was below the threshold for safe surgical treatment. Moral of the story: the imaging has got so good that diagnostics is now on the fringe of over diagnosing and the stats need to catch up
- lostlogin 4y ago
  This has been a thing for a long time, with MRI in particular. It gets quite philosophical. To diagnose something you need some pattern on the images. As resolution and tissue contrast improves you see more things, and the radiologist gets to decide if the appearance is something. When a clinician says there is a problem in some area of anatomy and there is something on the scan, the radiologist has to make a call. The great thing about being a tech is that making the call isn’t my job. I have noticed that keeping the field of view small tends to make me more friends. A half imaged liver haemangioma, a thyroid nodule or a white matter brain lesion as an incidental finding are a daily occurrence at least.

deleted 4y ago

[deleted]

esel2k 4y ago

So much this. I just interviewed about 10 doctors in the space of neurology and radiology to start some new projects. The truth is most of the headaches are from insurance coverage check or for radiologist for filling out correct reports. The fancy AI stuff is with maybe a few exceptions due to the great advancement imaging still far away from validation and I didn’t even start about it’s usage and gotomarket. Most of the cases the doctors sees are boring / regular cases - and problems like access to medical history is way more basic but more prevalent.

the_d3f4ult 4y ago
I'm a physician and former engineer and I agree with you. Even the giant tech companies that are in this space seem to have very little insight into how to utilize AI to improve healthcare. The mistake everyone seems to make is thinking that making a diagnosis is what makes medicine hard. It's not. e.g. An algorithm that can diagnose diabetic retinopathy from fundus photos 'as well as a specialist' makes for a good headline, but isn't particularly useful. Medicine is nothing like "House" in real life. AI should be leveraged to either improve efficiency or develop novel tools.
- naijaboiler 4y ago
  I can't upvote this enough
buscoquadnary 4y ago
> and problems like access to medical history is way more basic but more prevalent. This here is a problem I wish we could solve, get a central data broker that could hold aggregate medical history and just dole it out to companies. My wife has to get Medical supplies on a regular basis, the company we got them from got bought out by a PEC so we wanted to try switching companies. Months later we still can't get all the Drs insurance and supplier lined up. The problem is of course as a security guy I know a centralized clearinghouse would be a nightmare from every stand point. The real solution may be in the development of a good protocol, but good luck getting the Med industry that is still putzing around on ASM/400s to adopt something like that. Even with the force of law, i.e. HIPPA

dan_mctree 4y ago

But doctors tend to be pretty good at that part of the job you're describing. It's the Dr House part that often seems lacking, difficult patients often spend years going to different doctors only to keep hearing useless suggestions. It'd be amazing if we could have a solution for those people and AI might be able to help