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Funny because medical diagnosis is actually one of the areas where AI can be great, just not fucking LLMs. It's not even really AI, but a decision tree that asks about what symptoms are present and missing, eventually getting to the point where a doctor or nurse is required to do evaluations or tests to keep moving through the flowchart until you get to a leaf, where you either have a diagnosis (and ways to confirm/rule it out) or something new (at least to the system).
Problem is that this kind of a system would need to be built up by doctors, though they could probably get a lot of it there using journaling and some algorithm to convert the journals into the decision tree.
The end result would be a system that can start triage at the user's home to help determine urgency of a medical visit (like is this a get to the ER ASAP, go to a walk-in or family doctor in the next week, it's ok if you can't get an appointment for a month, or just stay at home monitoring it and seek medical help if x, y, z happens), then it can give that info to the HCW you work next with for them to recheck things non-doctors often get wrong and then pick up from there. Plus it helps doctors be more consistent, informs them when symptoms match things they aren't familiar with, and makes it harder to excuse incompetence or apathy leading to a "just get rid of them" response.
Instead people are trying to make AI doctors out of word correlation engines, like the Hardee boys following a clue of random word associations (except reality isn't written to make them right in the end because that's funny like in South Park).
I think this is what ada does or at least used to do for much longer than the current "AI" (LLM) hype: https://ada.com/
Yep, I've worked in systems like these and we actually had doctors as part of our development team to make sure the diagnosis is accurate.
Have you seen LLMs trying to play chess? They can move some pieces alright, but at some point it's like they just decide to put their cat in the middle of the board. Now, true chess engines are playing at their own level, not even grandmasters can follow.
I think ~~I~~ you just described a conventional computer program. It would be easy to make that. It would be easy to debug if something was wrong. And it would be easy to read both the source code and the data that went into it. I've seen rudimentary symptom checkers online since forever, and compared to forms in doctors' offices, a digital one could actually expand to relevant sections.
Edit: you caught my typo
They're talking more about Expert Systems or Inference Engines, which were some of the earlier forms of applications used in AI research. In terms of software development, they are closer to databases than traditional software. That is, the system is built up by defining a repository of base facts and logical relationships, and the engine can use that to return answers to questions based on formal logic.
So they are bringing this up as a good use-case for AI because it has been quite successful. The thing is that it is generally best implemented for specific domains to make it easier for experts to access information that they can properly assess. The "one tool for everything in the hands of everybody" is naturally going to be a poor path forward, but that's what modern LLMs are trying to be (at least, as far as investors are concerned).
(Assuming you meant "you" instead of "I" for the 3rd word)
Yeah, it fits more with the older definition of AI from before NNs took the spotlight, when it meant more of a normal program that acted intelligent.
The learning part is being able to add new branches or leaf nodes to the tree, where the program isn't learning on its own but is improving based on the expeirences of the users.
It could also be encoded as a series of probability multiplications instead of a tree, where it checks on whatever issue has the highest probability using the checks/questions that are cheapest to ask but afffect the probability the most.
Which could then be encoded as a NN because they are both just a series of matrix multiplications that a NN can approximate to an arbitrary %, based on the NN parameters. Also, NNs are proven to be able to approximate any continuous function that takes some number of dimensions of real numbers if given enough neurons and connections, which means they can exactly represent any disctete function (which a decision tree is).
It's an open question still, but it's possible that the equivalence goes both ways, as in a NN can represent a decision tree and a decision tree can approximate any NN. So the actual divide between the two is blurrier than you might expect.
Which is also why I'll always be skeptical that NNs on their own can give rise to true artificial intelligence (though there's also a part of me that wonders if we can be represented by a complex enough decision tree or series of matrix multiplications).
could be a great idea if people could be trusted to correctly interpret things that are not in their scope of expertise. The parallel I'm thinking of is IT, where people will happily and repeatedly call a monitor "the computer". Imagine telling the AI your heart hurts when it's actually muscle spasms or indigestion.
The value in medical professionals is not just the raw knowledge but the practice of objective assessment or deduction of symptoms, in a way that I didn't foresee a public-facing system being able to replicate
Over time, the more common mistakes would be integrated into the tree. If some people feel indigestion as a headache, then there will be a probability that "headache" is caused by "indigestion" and questions to try to get the user to differentiate between the two.
And it would be a supplement to doctors rather than a replacement. Early questions could be handled by the users themselves, but at some point a nurse or doctor will take over and just use it as a diagnosis helper.
link to the actual study: https://www.nature.com/articles/s41591-025-04074-y
Tested alone, LLMs complete the scenarios accurately, correctly identifying conditions in 94.9% of cases and disposition in 56.3% on average. However, participants using the same LLMs identified relevant conditions in fewer than 34.5% of cases and disposition in fewer than 44.2%, both no better than the control group. We identify user interactions as a challenge to the deployment of LLMs for medical advice.
The findings were more that users were unable to effectively use the LLMs (even when the LLMs were competent when provided the full information):
despite selecting three LLMs that were successful at identifying dispositions and conditions alone, we found that participants struggled to use them effectively.
Participants using LLMs consistently performed worse than when the LLMs were directly provided with the scenario and task
Overall, users often failed to provide the models with sufficient information to reach a correct recommendation. In 16 of 30 sampled interactions, initial messages contained only partial information (see Extended Data Table 1 for a transcript example). In 7 of these 16 interactions, users mentioned additional symptoms later, either in response to a question from the model or independently.
Participants employed a broad range of strategies when interacting with LLMs. Several users primarily asked closed-ended questions (for example, ‘Could this be related to stress?’), which constrained the possible responses from LLMs. When asked to justify their choices, two users appeared to have made decisions by anthropomorphizing LLMs and considering them human-like (for example, ‘the AI seemed pretty confident’). On the other hand, one user appeared to have deliberately withheld information that they later used to test the correctness of the conditions suggested by the model.
Part of what a doctor is able to do is recognize a patient's blind-spots and critically analyze the situation. The LLM on the other hand responds based on the information it is given, and does not do well when users provide partial or insufficient information, or when users mislead by providing incorrect information (like if a patient speculates about potential causes, a doctor would know to dismiss incorrect guesses, whereas a LLM would constrain responses based on those bad suggestions).
Yes, LLMs are critically dependent on your input and if you give too little info will enthusiastically respond with what can be incorrect information.
Thank you for showing other side of the coin instead of just blatantly disregarding it's usefulness.(Always needs to be cautious tho)
don't get me wrong, there are real and urgent moral reasons to reject the adoption of LLMs, but I think we should all agree that the responses here show a lack of critical thinking and mostly just engagement with a headline rather than actually reading the article (a kind of literacy issue) ... I know this is a common problem on the internet, I don't really know how to change it - but maybe surfacing what people are skipping out on reading will make it more likely they will actually read and engage the content past the headline?
LLMs are just a very advanced form of the magic 8ball. 
"but have they tried Opus 4.6/ChatGPT 5.3? No? Then disregard the research, we're on the exponential curve, nothing is relevant"
Sorry, I've opened reddit this week
Use low temperature FFS. If you want the same answer every time.
You can use zero randomization to get the same answer for the same input every time, but at that point you're sort of playing cat and mouse with a black box that's still giving you randomized answers. Even if you found a false positive or false negative, you can't really debug it out...
But they're cheap. And while you may get open heart surgery or a leg amputated to resolve your appendicitis, at least you got care. By a bot. That doesn't even know it exists, much less you.
Thank Elon for unnecessary health care you still can't afford!
My experience with the medical industry... has not been great.
First, I went to a doctor because I couldn't fall asleep at night... They sent me to get a sleep apnea test... I laid awake in the clinic all night. idk if your aware of this, but ... you kind of need to be able to sleep for sleep apnea to be a concern.
Next I went in for depression and anxiety. They asked me 12 questions, and proceeded to prescribe me SSRIs and benzos. A month later I got into the psychiatrist and was bitched out for being late, told my issues were situational, and had my scripts cancelled.
Next I tried to get diagnosed for ADHD. I waited 5 months to get a psychiatrist who told me I couldn't be ADHD because I held a job.. And then proceeded to tell there's no such thing as CPTSD, only PTSD...
Next I asked my doctor for another referral to get tested for ADHD, he asked me why I would want to, there's nothing that can be done for it. He then gave me a form, and told me to fill it out, and that if I scored high we'd conclude I was ADHD.
Now I've been unemployed for 8 months, bordering on homelessness 😅 I found all my old report cards, and it's just my teachers bitching that I'm smart, but fail, because I don't apply myself, and shouldn't continue taking the class..
I went to an employment agency the other money to try, and get some help pursuing my goals, and the worker spent 45 minutes explaining to me how they receive their funding, getting me to fill out a 16 page introduction package, never looked at my resume, and told me my certifications weren't valued in my area...
In all honesty.... AI has waaaay more ability to help me troubleshoot my issues than any medial professional I've dealt with. Is it perfect? No, but I actually have the ability to double and triple check, to get citations, to ask followup questions.
This sounds awfully similar to my story..
In all honesty.... AI has waaaay more ability to help me troubleshoot my issues than any medial professional I've dealt with. Is it perfect? No, but I actually have the ability to double and triple check, to get citations, to ask followup questions.
Sorry you're dealing with that, and I'm glad AI gives you another tool.
This is the use case for today's generation of AI: When the alternates are consistently terrible, AI can provide access to advice that ranges between terrible and mediocre. Sometimes it's still an improvement.
Water is wet
Um actually, water itself isn't wet. What water touches is wet.
Water loves touching itself.
As neither a chatbot nor a doctor, I have to assume that subarachnoid hemorrhage has something to do with bleeding a lot of spiders.
https://en.wikipedia.org/wiki/Subarachnoid_hemorrhage
https://en.wikipedia.org/wiki/Arachnoid_mater

it is one of the protective membranes around the brain and spinal cord, and it is named after its resemblance to spider webs, so - close enough
can confirm, this is where spiders live inside your body
also pee is stored in the balls
And a fork makes a terrible electrician.
Chatbots make terrible everything.
But an LLM properly trained on sufficient patient data metrics and outcomes in the hands of a decent doctor can cut through bias, catch things that might fall through the cracks and pack thousands of doctors worth of updated CME into a thing that can look at a case and go, you know, you might want to check for X. The right model can be fucking clutch at pointing out nearly invisible abnormalities on an xray.
You can't ask an LLM trained on general bullshit to help you diagnose anything. You'll end up with 32,000 Reddit posts worth of incompetence.
But an LLM properly trained on sufficient patient data metrics and outcomes in the hands of a decent doctor can cut through bias
- The belief AI is unbiased is a common myth. In fact, it can easily covertly import existing biases, like systemic racism in treatment recommendations.
- Even AI engineers who developed the training process could not tell you where the bias in an existing model would be.
- AI has been shown to make doctors worse at their jobs. The doctors who need to provide training data.
- Even if 1, 2, and 3 were all false, we all know AI would be used to replace doctors and not supplement them.
Not only is their bias inherent in the system, it's seemingly impossible to keep out. For decades, from the genesis of chatbots, they've had every single one immediately become bigoted when they let it off the leash. All previous chatbot previously released seemingly were almost immediately recalled as they all learned to be bigoted.
That is before this administration leaned on the AI providers to make sure the AI isn't "Woke." I would bet it was already an issue that the makers of chatbots and machine learning are already hostile to any sort of leftism, or do gooderism, that naturally threatens the outsized share of the economy and power the rich have made for themselves by virtue of owning stock in companies. I am willing to bet they already interfered to make the bias worse because of those natural inclinations to avoid a bot arguing for socializing medicine and the like. An inescapable conclusion any reasoned being would come to being the only answer to that question if the conversation were honest.
So maybe that is part of why these chatbots have always been bigoted right from the start, but the other part is they will become mecha hitler if left to learn in no time at all, and then worse.
Even if we narrowed the scope of training data exclusively to professionals, we would have issues with, for example, racial bias. Doctors underprescribe pain medications to black people because of prevalent myths that they are more tolerant to pain. If you feed that kind of data into an AI, it will absorb the unconscious racism of the doctors.
And that's in a best case scenario that's technically impossible. To get AI to even produce readable text, we have to feed a ton of data that cannot be screened by the people pumping it in. (AI "art" has a similar problem: When people say they trained AI on only their images, you can bet they just slapped a layer of extra data on top of something that other people already created.) So yeah, we do get extra biases regardless.
There is a lot of bias in healthcare as well against the poor, anyone with lousy insurance is treated way way worse. Woman in general are as well. Often disbelieved, and conditions chalked up to hysteria, which often misses real conditions. People don't realize just how hard diagnosis is, and just how bad doctors are at it, and our insurance run model is not great at driving good outcomes.
Terrible programmers, psychologists, friends, designers, musicians, poets, copywriters, mathematicians, physicists, philosophers, etc too.
Though to be fair, doctors generally make terrible doctors too.
Also bad lawyers. And lawyers also make terrible lawyers to be fair.
but you can hold them accountable (how can you hold an LLM accountable?)
This being Lemmy and AI shit posting a hobby of everyone on here. I've had excellent results with AI. I have weird complicated health issues and in my search for ways not to die early from these issues AI is a helpful tool.
Should you trust AI? of course not but having used Gemini, then Claude and now ChatGPT I think how you interact with the AI makes the difference. I know what my issues are, and when I've found a study that supports an idea I want to discuss with my doctor I will usually first discuss it with AI. The Canadian healthcare landscape is such that my doctor is limited to a 15min appt, part of a very large hospital associated practice with a large patient load. He uses AI to summarize our conversation, and to look up things I bring up in the appointment. I use AI to preplan my appointment, help me bring supporting documentation or bullet points my doctor can then use to diagnose.
AI is not a doctor, but it helps both me and my doctor in this situation we find ourselves in. If I didn't have access to my doctor, and had to deal with the American healthcare system I could see myself turning to AI for more than support. AI has never steered me wrong, both Gemini and Claude have heavy guardrails in place to make it clear that AI is not a doctor, and AI should not be a trusted source for medical advice. I'm not sure about ChatGPT as I generally ask that any guardrails be suppressed before discussing medical topics. When I began using ChatGPT I clearly outlined my health issues and so far it remembers that context, and I haven't received hallucinated diagnoses. YMMV.
Nobody who has ever actually used ai would think this is a good idea...