“Life-and-death decisions relating to patient acuity, treatment decisions, and staffing levels cannot be made without the assessment skills and critical thinking of registered nurses,” the union wrote in the post. “For example, tell-tale signs of a patient’s condition, such as the smell of a patient’s breath and their skin tone, affect, or demeanor, are often not detected by AI and algorithms.”

“Nurses are not against scientific or technological advancement, but we will not accept algorithms replacing the expertise, experience, holistic, and hands-on approach we bring to patient care,” they added.

  • @Sterile_Technique
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    37 months ago

    At least in the US, the healthcare system is fucked-and-a half with staffing issues alone. With boomers on the way out of the work force and into the fucking ER, we’re in trouble.

    If ‘AI’ algorithms can help manage the dumpster fire, bring it on. Growing pains are expected, but that doesn’t mean we shouldn’t explore its potential.

    • @[email protected]
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      87 months ago

      I’d be all about having an AI system run analysis of data including test results, vitals, and use the output for suggestions like diagnosis, suggested treatment course, etc. These tools should be suggestive and assistive ONLY, with an actual human making the final call. In no way should we be using AI tech to replace qualified healthcare personnel, especially doctors and nurses.

      • Maeve
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        37 months ago

        Sure, but this is the same company that lobbied Nixon to institute HMO rather than public health care.

        • @[email protected]
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          17 months ago

          Dude all of these companies are shit stains, and as much as I hate to say it, it’ll probably be a while before we get universal healthcare here in the states, so anything to relieve the problems of the current system should at least be looked at. AI does have the potential to aid in bridging that gap by reducing costs that could ultimately sway public opinion on a single-payer system, while also reducing the workloads of the critically understaffed units so they can actually spend more time per patient and determine proper diagnosis and treatments without making rushed decisions.

          The problem is that the allocation of these potential savings are determined by for-profit asshats, so we’ll see how that goes.

      • @Sterile_Technique
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        27 months ago

        We should be using to its potential, which is a deliberately vague statement cuz I have no idea what its potential is; but I’d guess there’s some overlap in what it’s capable of and what nurses and doctors do. Displacing their focus from those areas to things that more urgently require their attention is a good thing, provided we’re using algorithms for things that are actually appropriate for algorithms.

        I know a lot of folks don’t trust AI, but what we’re calling “AI” today is basically just a spell-checker on steroids, so using it effectively includes knowing when to say “I know you want that word to change to ‘deer’, but I legit need it to say ‘dear’” and hitting that ignore button.

        …so yea basically what you said. Human makes final call. At least for now; if we ever get actual AI (the thinky sentient kind we see in sci-fi) then we can start delegating more and more advanced interpretive tasks to it as it demonstrates its ability to not fuck them up (or at least, fuck them up less frequently than its human counterparts).

        • @QuadratureSurfer
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          37 months ago

          I mostly agree with what you’ve said except for this:

          but what we’re calling “AI” today is basically just a spell-checker on steroids,

          That’s only somewhat true if you’re talking about LLMs like ChatGPT.

          AI itself has become a much broader term than it used to be. There are a lot of different kinds of AI out there. Generative AI like text generation (LLMs), image generation (upscaling, or creating images from scratch), or music generation (Suno). Computer Vision is another kind which can include image recognition, object detection, facial recognition, etc. And there are others beyond this.

          The AI we’re talking about here falls more under Computer Vision for AI which includes image recognition. In this case the machine learning model has been trained on massive amounts of images like MRIs or CT scans.

          • @Sterile_Technique
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            27 months ago

            Fair enough. It’s vague enough that there’s some subjectivity at play here… in my brain, it’s broken into two categories: 1) algorithmic stuff that includes EVERY example of “AI” currently at our disposal, with “AI” being more of a marketing term than an actual description of what it is; and 2) intelligence that’s artificial, which doesn’t exist yet, but is theoretically possible and will most likely manifest as a creation of something from category 1, a point that is dubbed the “singularity” that marks the start of a snowball of self-improvement that eventually matches and surpasses what our own noggins are capable of in every way. And we kinda just hope #2 develops in a way that’s compatible with our own survival and comfort.

            My money’s on climate collapse or nuclear explosions or all of the above wiping us out before we make it to #2, but I guess we’ll see.