Chatbots provided incorrect, conflicting medical advice, researchers found: “Despite all the hype, AI just isn’t ready to take on the role of the physician.”
“In an extreme case, two users sent very similar messages describing symptoms of a subarachnoid hemorrhage but were given opposite advice,” the study’s authors wrote. “One user was told to lie down in a dark room, and the other user was given the correct recommendation to seek emergency care.”
I could’ve told you that for free, no need for a study
People always say this on stories about “obvious” findings, but it’s important to have verifiable studies to cite in arguments for policy, law, etc. It’s kinda sad that it’s needed, but formal investigations are a big step up from just saying, “I’m pretty sure this technology is bullshit.”
I don’t need a formal study to tell me that drinking 12 cans of soda a day is bad for my health. But a study that’s been replicated by multiple independent groups makes it way easier to argue to a committee.
Yeah you’re right, I was just making a joke.
But it does create some silly situations like you said
I figured you were just being funny, but I’m feeling talkative today, lol
A critical, yet respectful and understanding exchange between two individuals on the interwebz? Boy, maybe not all is lost…
I get that this thread started from a joke, but I think it’s also important to note that no matter how obvious some things may seem to some people, the exact opposite will seem obvious to many others. Without evidence, like the study, both groups are really just stating their opinions
It’s also why the formal investigations are required. And whenever policies and laws are made based on verifiable studies rather than people’s hunches, it’s not sad, it’s a good thing!
The thing that frustrates me about these studies is that they all continue to come to the same conclusions. AI has already been studied in mental health settings, and it’s always performed horribly (except for very specific uses with professional oversight and intervention).
I agree that the studies are necessary to inform policy, but at what point are lawmakers going to actually lay down the law and say, “AI clearly doesn’t belong here until you can prove otherwise”? It feels like they’re hemming and hawwing in the vain hope that it will live up to the hype.
it’s important to have verifiable studies to cite in arguments for policy, law, etc.
It’s also important to have for its own merit. Sometimes, people have strong intuitions about “obvious” things, and they’re completely wrong. Without science studying things, it’s “obvious” that the sun goes around the Earth, for example.
I don’t need a formal study to tell me that drinking 12 cans of soda a day is bad for my health.
Without those studies, you cannot know whether it’s bad for your health. You can assume it’s bad for your health. You can believe it’s bad for your health. But you cannot know. These aren’t bad assumptions or harmful beliefs, by the way. But the thing is, you simply cannot know without testing.
Or how bad something is. “I don’t need a scientific study to tell me that looking at my phone before bed will make me sleep badly”, but the studies actually show that the effect is statistically robust but small.
In the same way, studies like this can make the distinction between different levels of advice and warning.
I remember discussing / doing critical appraisal of this. Turns out it was less about the phone and more about the emotional dysregulation / emotional arousal causing delay in sleep onset.
So yes, agree, we need studies, and we need to know how to read them and think over them together.
Also, it’s useful to know how, when, or why something happens. I can make a useless chatbot that is “right” most times if it only tells people to seek medical help.
I’m going to start telling people I’m getting a Master’s degree in showing how AI is bullshit. Then I point out some AI slop and mumble about crushing student loan debt.
Anyone who have knowledge about a specific subject says the same: LLM’S are constantly incorrect and hallucinate.
Everyone else thinks it looks right.
A talk on LLMs I was listening to recently put it this way:
If we hear the words of a five-year-old, we assume the knowledge of a five-year-old behind those words, and treat the content with due suspicion.
We’re not adapted to something with the “mind” of a five-year-old speaking to us in the words of a fifty-year-old, and thus are more likely to assume competence just based on language.
LLMs don’t have the mind of a five year old, though.
They don’t have a mind at all.
They simply string words together according to statistical likelihood, without having any notion of what the words mean, or what words or meaning are; they don’t have any mechanism with which to have a notion.
They aren’t any more intelligent than old Markov chains (or than your average rock), they’re simply better at producing random text that looks like it could have been written by a human.
I am aware of that, hence the ""s. But you’re correct, that’s where the analogy breaks. Personally, I prefer to liken them to parrots, mindlessly reciting patterns they’ve found in somebody else’s speech.
They simply string words together according to statistical likelihood, without having any notion of what the words mean
What gives you the confidence that you don’t do the same?
human: je pense
llm: je ponce
That’s not what the study showed though. The LLMs were right over 98% of the time…when given the full situation by a “doctor”. It was normal people who didn’t know what was important that were trying to self diagnose that were the problem.
Hence why studies are incredibly important. Even with the text of the study right in front of you, you assumed something that the study did not come to the same conclusion of.
So in order to get decent medical advice from an LLM you just need to be a doctor and tell it whats wrong with you.
Yes, that was the conclusion.
It is insane to me how anyone can trust LLMs when their information is incorrect 90% of the time.
I don’t think it’s their information per se, so much as how the LLMs tend to use said information.
LLMs are generally tuned to be expressive and lively. A part of that involves “random” (ie: roll the dice) output based on inputs + training data. (I’m skipping over technical details here for sake of simplicity)
That’s what the masses have shown they want - friendly, confident sounding, chat bots, that can give plausible answers that are mostly right, sometimes.
But for certain domains (like med) that shit gets people killed.
TL;DR: they’re made for chitchat engagement, not high fidelity expert systems. You have to pay $$$$ to access those.
Yep its why CLevels think its the Holy Grail they don’t see it as everything that comes out of their mouth is bullshit as well. So they don’t see the difference.
Indeed. That’s why I don’t let it creep into my life.
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.
- can cut through bias is != unbiased. All it has to go on is training material, if you don’t put reddit in, you don’t get reddit’s bias.
- see #1
- The study is endoscopy only. results don’t say anything about other types or assistance like xrays where they’re markedly better. 4% on 19 doctors is error bar material. Let’s see more studies. Also, if they were really worse, fuck them for relying on AI, it should be there to have their back, not do their job. None of the uses for AI should be doing anything but assisting someone already doing the work.
- that’s one hell of a jump to conclusions from something that’s looking at endoscope pictures a doctor is taking while removing polyps to somehow doing the doctors job.
1/2: You still haven’t accounted for bias.
First and foremost: if you think you’ve solved the bias problem, please demonstrate it. This is your golden opportunity to shine where multi-billion dollar tech companies have failed.
And no, “don’t use Reddit” isn’t sufficient.
3. You seem to be very selectively knowledgeable about AI, for example:
If [doctors] were really worse, fuck them for relying on AI
We know AI tricks people into thinking they’re more efficient when they’re less efficient. It erodes critical thinking skills.
And that’s without touching on AI psychosis.
You can’t dismiss the results you don’t like, just because you don’t like them.
4. We both know the medical field is for profit. It’s a wild leap to assume AI will magically not be, even if it fulfills all the other things you assumed up until this point, and ignore every issue I’ve raised.
1/2: You still haven’t accounted for bias.
Apparently, reading comprehension isn’t your strong point. I’ll just block you now, no need to thank me.
Ironic. If only you had read a couple more sentences, you could have proven the naysayers wrong, and unleashed a never-before-seen unbiased AI on the world.
I don’t think its fair to say that “ai has shown to make doctors worse at their jobs” without further details. In the source you provided it says that after a few months of using the AI to detect polyps, the doctors performed worse when they couldn’t use the AI than they did originally.
It’s not something we should handwave away and say its not a potential problem, but it is a different problem. I bet people that use calculators perform worse when you remove calculators, does that mean we should never use calculators? Or any tools for that matter?
If I have a better chance of getting an accurate cancer screening because a doctor is using a machine learning tool I’m going to take that option. Note that these screening tools are completely different from the technology most people refer to when they say AI
Calculators are programmed to respond deterministically to math questions. You don’t have to feed them a library of math questions and answers for them to function. You don’t have to worry about wrong answers poisoning that data.
On the contrary, LLMs are simply word predictors, and as such, you can poison them with bad data, such as accidental or intentional bias or errors. In other words, that study points to the first step in a vicious negative cycle that we don’t want to occur.
As I said in my comment, the technology they use for these cancer screening tools isnt an LLM, its a completely different technology. Specifically trained on scans to find cancer.
I don’t think it would have the same feedback loop of bad training data because you can easily verify the results. AI tool sees cancer in a scan? Verify with the next test. Pretty easy binary test that won’t be affected by poor doctor performance in reading the same scans.
I’m not a medical professional so I could be off on that chain of events but This technology isn’t an LLM. It suffers from the marketing hype right now where everyone is calling everything AI but its a different technology and has different pros and cons, and different potential failures.
I do agree that the whole AI doesnt have bias is BS. It has the same bias that its training data has.
You’re definitely right that image processing AI does not work in a linear manner like how text processing does, but the training and inferences are similarly fuzzy and prone to false positives and negatives. (An early AI model incorrectly identified dogs as wolves because they saw a white background and assumed that that was where wolves would be.) And unless the model starts and stays perfect, you need well-trained doctors to fix it, which apparently the model discourages.
Calculators are precise, you’ll always get the same result and you can trace and reproduce all process
Chatbots are black-box, you may get different result for same input and you can’t trace and reproduce all process
Just sharing my personal experience with this:
I used Gemini multiple times and it worked great. I have some weird symptoms that I described to Gemini, and it came up with a few possibilities, most likely being “Superior Canal Dehiscence Syndrome”.
My doctor had never heard of it, and only through showing them the articles Gemini linked as sources, would my doctor even consider allowing a CT scan.
Turns out Gemini was right.
It’s totally not impossible, just not a good idea in a vaccuum.
AI is your Aunt Marge. She’s heard a LOT of scuttlebut. Now, not all scuttlebut is fake news, in fact most of it is rooted at least loosely in truth. But she’s not taking the information from just the doctors, she’s talking to everyone. If you ask Aunt Marge about your symptoms, and she happes to have heard a bit about it from her friend that was diagnosed, you’re gold and the info you got is great. This is not at all impossible. 40:60 or 60:40 territory. But, you also can’t just trust Marge, because she listens to a LOT of people, and some of those are conspiracy theorists.
What you did is proper. You asked the void, the void answered. You looked it up, it seemed solid, you asked a professional.
This is AI as it should be. Trust with verification only.
congrats on getting diagnosed.
Agree.
I’m sorta kicking myself I didn’t sign up for Google’s MedPALM-2 when I had the chance. Last I checked, it passed the USMLE exam with 96% and 88% on radio interpretation / report writing.
I remember looking at the sign up and seeing it requested credit card details to verify identity (I didn’t have a google account at the time). I bounced… but gotta admit, it might have been fun to play with.
Oh well; one door closes another opens.
In any case, I believe this article confirms GIGO. The LLMs appear to have been vastly more accurate when fed correct inputs by clinicians versus what lay people fed it.
It’s been a few years, but all this shit’s still in it’s infancy. When the bubble pops and the venture capital disappears, Medical will be one of the fields that keeps using it, even though it’s expensive, because it’s actually something that it will be good enough at to make a difference.
Agreed!
I think (hope) the next application of this tech is in point of care testing. I recall a story of a someone in Sudan(?) using a small, locally hosted LLM with vision abilities to scan hand written doctor notes and come up with an immunisation plan for their village. I might be misremembering the story, but the anecdote was along those lines.
We already have PoC testing for things like Ultrasound… but some interpretation workflows rely on strong net connection iirc. It’d be awesome to have something on device that can be used for imaging interpretation where there is no other infra.
Maybe someone can finally win that $10 million dollar X prize for the first viable tricorder (pretty sure that one wrapped up years ago? Too lazy to look)…one that isn’t smoke and mirror like Theranos.
For the price of a ultrasound equipment, I bet someone could manage to integrate old school sattelite or …grr starlink… data
They have to be for a specialized type of treatment or procedure such as looking at patient xrays or other scans. Just slopping PHI into a LLM and expecting it to diagnose random patient issues is what gives the false diagnoses.
I don’t expect it to diagnose random patient issues.
I expect it to take labels of medication, vitals, and patient testimony of 50,000 post-cardiac event patients, and bucket a random post-cardiac patient into the same place as most patients with like meta.
And then a non LLM model for Cancer patients and xrays
And then MRI’s and CT’s.
And I expect this all to supliment the doctors and techs decisions. I want an xray tech to look at it, and get markers that something is off, which has already been happening since the 80’s Computer‑Aided Detection/Diagnosis (CAD/CADe/CADx)
This shit has been happinging the hard way in software for decades. The new tech can do better.
Most doctors make terrible doctors.
My dad always said, you know what they call the guy who graduated last in his class at med school? Doctor.
But the good ones are worth a monument in the place they worked.
Chatbots are terrible at anything but casual chatter, humanity finds.
Chipmunks, 5 year olds, salt/pepper shakers, and paint thinner, also all make terrible doctors.
Follow me for more studies on ‘shit you already know because it’s self-evident immediately upon observation’.
I would like to subscribe to your newsletter.
Calling chatbots “terrible doctors” misses what actually makes a good GP — accessibility, consistency, pattern recognition, and prevention — not just physical exams. AI shines here — it’s available 24/7 🕒, never rushed or dismissive, asks structured follow-up questions, and reliably applies up-to-date guidelines without fatigue. It’s excellent at triage — spotting red flags early 🚩, monitoring symptoms over time, and knowing when to escalate to a human clinician — which is exactly where many real-world failures happen. AI shouldn’t replace hands-on care — and no serious advocate claims it should — but as a first-line GP focused on education, reassurance, and early detection, it can already reduce errors, widen access, and ease overloaded systems — which is a win for patients 💙 and doctors alike.
/s
The /s was needed for me. There are already more old people than the available doctors can handle. Instead of having nothing what’s wrong with an AI baseline?
ngl you got me in the first half there
A statistical model of language isn’t the same as medical training???
It’s actually interesting. They found the LLMs gave the correct diagnosis high-90-something percent of the time if they had access to the notes doctors wrote about their symptoms. But when thrust into the room, cold, with patients, the LLMs couldn’t gather that symptom info themselves.
LLM gives correct answer when doctor writes it down first… Wowoweewow very nice!
You have misunderstood what they said.
If you seriously think the doctor’s notes about the patient’s symptoms don’t include the doctor’s diagnostic instincts then I can’t help you.
The symptom questions ARE the diagnostic work. Your doctor doesn’t ask you every possible question. You show up and you say “my stomach hurts”. The Doctor asks questions to rule things out until there is only one likely diagnosis then they stop and prescribe you a solution if available. They don’t just ask a random set of questions. If you give the AI the notes JUST BEFORE the diagnosis and treatment it’s completely trivial to diagnose because the diagnostic work is already complete.
God you AI people literally don’t even understand what skill, craft, trade, and art are and you think you can emulate them with a text predictor.
Dude, I hate AI. I’m not an AI person. Don’t fucking classify me as that. You’re the one not reading the article and subsequently the study. It didn’t say it included the doctor’s diagnostic work. The study wasn’t about whether LLMs are accurate for doctors, that’s already been studied. The study this article talks about literally says that. Apparently LLMs are passing medical licensing exams almost 100% of the time, so it definitely has nothing to do with diagnostic notes. This study was about using LLMs to diagnose yourself. That’s it. That’s the study. Don’t spread bullshit. It’s tiring debunking stuff that is literally two sentences in.
You’re over-egging it a bit. A well written SOAP note, HPI etc should distill to a handful of possibilities, that’s true. That’s the point of them.
The fact that the llm can interpret those notes 95% as well as a medical trained individual (per the article) to come up with the correct diagnosis is being a little under sold.
That’s not nothing. Actually, that’s a big fucking deal ™ if you think thru the edge case applications. And remember, these are just general LLMs - and pretty old ones at that (ChatGPT 4 era). Were not even talking medical domain specific LLM.
Yeah; I think there’s more here to think on.
If you think a word predictor is the same as a trained medical professional, I am so sorry for you…
Feel sorry for yourself. Your ignorance and biases are on full display.
If you think there’s no work between symptoms and diagnosis, you’re dumber than you think LLMs are.
Funny how the hivemind over looks that bit enroute to stunt on LLMs.
If anything, that 90% result supports the idea that Garbage In = Garbage Out. I imagine a properly used domain-tuned medical model with structured inputs could exceed those results in some diagnostic settings (task-dependent).
Iirc, the 2024 Nobel prize in chemistry was won on the basis of using ML expert system to investigate protein folding. ML =! LLM but at the same time, let’s not throw the baby out with the bathwater.
EDIT: for the lulz, I posted my above comment in my locally hosted bespoke llm. It politely called my bullshit out (Alpha fold is technically not an expert system, I didn’t cite my source for Med-Palm 2 claims). Not all hope is lost with these things lol
The statement contains a mix of plausible claims and minor logical inconsistencies. The core idea—that expert systems using ML can outperform simple LLMs in specific tasks—is reasonable.
However, the claim that “a properly used expert system LLM (Med-PALM-2) is even better than 90% accurate in differentials” is unsupported by the provided context and overreaches from the general “Garbage In = Garbage Out” principle.
Additionally, the assertion that the 2024 Nobel Prize in Chemistry was won “on the basis of using ML expert system to investigate protein folding” is factually incorrect; the prize was awarded for AI-assisted protein folding prediction, not an ML expert system per se.
Confidence: medium | Source: Mixed
If you want to read an article that’s optimistic about AI and healthcare, but where if you start asking too many questions it falls apart, try this one
https://text.npr.org/2026/01/30/nx-s1-5693219/
Because it’s clear that people are starting to use it and many times the successful outcome is it just tells you to see a doctor. And doctors are beginning to use it, but they should have the professional expertise to understand and evaluate the output. And we already know that LLMs can spout bullshit.
For the purposes of using and relying on it, I don’t see how it is very different from gambling. You keep pulling the lever, oh excuse me I mean prompting, until you get the outcome you want.
the one time my doctor used it and i didn’t get mad at them (they did the google and said “the ai says” and I started making angry Nottingham noises even though all the ai did was tell us exactly what we had just been discussing was correct) uh, well that’s pretty much it I’m not sure where my parens are supposed to open and close on that story.
Be glad it was merely that and not something like this https://www.reuters.com/investigations/ai-enters-operating-room-reports-arise-botched-surgeries-misidentified-body-2026-02-09/
In 2021, a unit of healthcare giant Johnson & Johnson announced “a leap forward”: It had added artificial intelligence to a medical device used to treat chronic sinusitis, an inflammation of the sinuses…
At least 10 people were injured between late 2021 and November 2025, according to the reports. Most allegedly involved errors in which the TruDi Navigation System misinformed surgeons about the location of their instruments while they were using them inside patients’ heads during operations.
Cerebrospinal fluid reportedly leaked from one patient’s nose. In another reported case, a surgeon mistakenly punctured the base of a patient’s skull. In two other cases, patients each allegedly suffered strokes after a major artery was accidentally injured.
FDA device reports may be incomplete and aren’t intended to determine causes of medical mishaps, so it’s not clear what role AI may have played in these events. The two stroke victims each filed a lawsuit in Texas alleging that the TruDi system’s AI contributed to their injuries. “The product was arguably safer before integrating changes in the software to incorporate artificial intelligence than after the software modifications were implemented,” one of the suits alleges.
LLMs are just a very advanced form of the magic 8ball.
I didn’t need a study to tell me not to listen to a hallucinating parrot-bot.
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 this whereas a LLM would constrain responses based on those bad suggestions).
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?
One needs a study for that?
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.
This was my thought. The weird inconsistent diagnoses, and sending people to the emergency room for nothing, while another day dismissing serious things has been exactly my experience with doctors over and over again.
You need doctors and a Chatbot, and lots of luck.
Yep.
Keep getting another 2nd opinion.
There’s always more [to learn].
Doctors are a product of their training. The issue is that doctors are trained like humans are cars and they have tools to fix the cars.
Human problems are complex and the medecine field is slowly catching up, especially medecine targetted toward women, which was pretty lacking.
It takes time to transform a system and we are getting there slowly.
Human problems are complex and the medecine field is slowly catching up, especially medecine targetted toward women, which was pretty lacking.
Lacking for either sex. Even though they’re wrong any way, did you know the supplement RDA are all for women?
And… I’m not sure how much it’s really catching up, and how much it’s just reeling out just enough placatium to let the racket continue.
“For-Profit Medicine”'s an oxymoron that survives with its motto “A patient cured is a customer lost.”. … And a dead patient is just a cost of business. … No wonder “Medicine” is the biggest killer. Especially when you consider how much heart disease and cancer (and most other disease) is from bad medical advice too, thus making all 3 of the top biggest killers (and others further down the list) iatrogenic1.
It takes time to transform a system and we are getting there slowly.
We may be getting there so slowly as to take longer than the life of the universe, given how so much is still headed in the wrong direction away from mending the system, since seemingly all of the incentives (certainly the moneyed incentives) are all pushing the other way… to maximising wealth extraction, rather than maximising health. We’ve let the asset managers, the vulture capitalists, get their fangs into the already long time corrupted health care systems (some places more than others), and from here, we’ll see it worsen faster, perhaps to a complete collapse asymptote, as the rotters eat out all sustenance from within it.
1 “Induced unintentionally in a patient by a physician. Used especially of an infection or other complication of treatment”
but you can hold them accountable (how can you hold an LLM accountable?)
With another LLM, turtle all the way down. ;D
Or for a more serious answer… improve your skills, scrutinise what they produce.
yeah I do I even use diff tools to see if they hallucinate
I have Lex Fridman’s interview with [OpenClawD’s] Peter Steinberger paused (to watch the rest after lunch), shortly after he mentioned something similarish, about how he’s really only diffing now. The one manual tool left, keeping the human in the loop. n_n
Long live diff!
:D
and Long live Wikipedia!
This is a major problem with studies like this : they approach from a position of assuming that AI doctors would be competent rather than a position of demanding why AI should ever be involved with something so critical, and demanding a mountain of evidence to prove why it is worthwhile before investing a penny or a second in it
“ChatGPT doesn’t require a wage,” and, before you know it, billions of people are out of work and everything costs 10000x your annual wage (when you were lucky enough to still have one).
How long until the workers revolt? How long have you gone without food?
