Comment by tantalor 2 months ago
Unclear what "AI" brings to the table here. Sounds like traditional automation & monitoring could do the job here. No mention of how the model works, or what kind of training is involved.
> white blood cell count was "really, really high"
You don't need AI for this.
I wish they would provide a more compelling example.
In AI applications, especially those involving predictive modeling, MARS can be used to improve the accuracy of predictions. For example, MARS models are used in time series forecasting, financial predictions, environmental modeling, and other domains where relationships between inputs and outputs are complex and non-linear. By adding time-awareness, the model can handle time-based data more effectively.
> Unclear what "AI" brings to the table here
A 26% reduction in unexpected deaths, apparently.
They used a bog standard statistical technique and called it AI to try to attract more funding...
It's the difference between "give the programmer this medical report and have them parse out the white blood cell count" versus s/progammer/AI. And the same every time that the report changes in any way.
I've been that programmer more times than I can count. I'm much happier about being able to work on better problems instead than I am worried about AI taking away my rice bowl.
I think there is some element of "technology laundering" here that I saw during the blockchain hype. Even if plain ol' monitoring and automation could solve your problem no executives want to back that. If you say it's adding AI, blockchain, etc. they get to feel like a visionary so they'll fund your project
I am beyond tired of the "It made a decision on a if-statement, that's AI!"
Most modern AI does even less. It simply flows values through a graph. No decision is ever made. The consumer of the network interprets the result and makes a decision.
I heard AI is just if-statements, so perhaps the reverse is true.
> don't need AI for this
And you don’t need Dropbox for file sync. Machine learning makes integrating automation easier.
You do have to wonder though, if traditional automation could do the job, why wasn't that done already?
I think the real question is why is this being reported on. There are always medical advancements, but because this one gets chosen as a news story because "AI" in the headline gets clicks.
This isn't just a small advancement, though. It's a simple tool, which isn't restricted to medical specialists, with a huge impact.
If a study found that letting cats roam hospital hallways reduced unexpected deaths by 26%, I think that would be reported, too.
It's a 26% decrease in relative terms, but looking at the study shows that it is a 0.5% decrease in absolute terms (1.6% vs 2.1%). A 0.5% decrease is great and should be applauded, but I think the article framing of this being a breakthrough is misleading and even goes against the conclusions of the very paper it is reporting on.
Because healthcare (and banking and ....) are horribly behind on tech. We have life saving devices in hospitals still running Windows 95 as an OS. Also, the main problem in healthcare is misaligned incentives. As said elsewhere in this thread, this kind of tech will get when it enables cost reductions larger than its costs.
Because tech people don't understand how healthcare systems work, and reciprocally healthcare workers have neither the education nor the time to understand new tech. The result is what you get today: people from both sides shouting at deaf ears on the internet. Also, the usual corporate culture issues.
Hot take: If tech people who are used to working with complex systems can't understand it, maybe it's time to replace the whole thing. The healthcare system doesn't make sense at all and is that way because of regulation and a bunch of other crap we need to get rid of/refactor.
One thing tech people absolutely don't understand is how much 2024 medicine is know-how and not science. And that's not for lack of trying to make it science. There are certainly things that could be improved, even through trivial stats. But for the most part, our information retrieval capabilities are so bad that the ability to actually walk the corridors and see the patient IRL is currently not something current state-of-the-art AI can compensate for.
I wasn't referring to marginal gains through the use of AI or automation, I'm referring to re-building everything from scratch so that things are actually efficient and effective. ie, see what Tesla did to the car industry and SpaceX to the space industry. We need something like that for health.
This was traditional automation. They used a bog standard statistical techniques and called it AI for fundraising purposes.
The paper itself doesn't claim it's AI. They do say it's "machine learning".
Machine learning is indeed extremely good at pattern recognition, but I wouldn't trust an LLM to reliably identify patterns, especially in a medical context. As other commenters have said, this article is evidence of classical methods continuing to be useful.
This doesn't make sense on many levels. "Hospital IT" does not code the hospital EHR systems, just like the airport doesn't code flight management systems.
These are life-long software engineers, just like others reading this comment, using the best tools at their disposal to engineer lifesaving software. They're not using "regex" to develop algorithms for monitoring patients (???), and frankly that suggestion is so wild that one has to assume you don't know anything about algorithm design at all.
An LLM literally hallucinates incorrect answers by design and struggles to get extremely basic math and spelling correct.
You're welcome to put your literal life in the hands of a hallucinating english generator, but when it comes to healthcare, I want a "0% LLM" policy. LLM's will be the cheap things that offer substandard care to poor people, while the wealthy and elite enjoy personalized and human-centered care.
This sentence contains two diametrically opposed hypothesis.
LLM's and accuracy in one sentence in the context of quantifying thresholds is stunning.
LLM's don't have a concept of numerical accuracy.
Knowing what I know about workplace dynamics in hospitals I'm gonna go out on a limb and say that the "new hotness" factor of the term "AI" probably does a lot of heavy lifting here when it comes to getting buy in from management and users.
Forgoing a decade of income to get some letters beside your name selects for people who don't take orders from Clippy unless you market it well.
It’s a regression model. You don’t “need” AI for anything. But using ML to identify thresholds for decision making is extremely useful.
I don’t like calling everything AI, but I’m even more irritated by people that don’t understand the value of simple ML models for low hanging fruit decisions like the one shown here