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- cross-posted to:
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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.”
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.
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.
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 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
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.
it’s not just about bad Web data or Reddit data even old books has some unconscious bias
and even if you find every “wrong” or “bad” data (which is you can’t because somethings are just subjective) and after remove them still you can’t be sure about it
What is your fixation with trying to tell me i’m saying you can remove all bias?
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:
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.
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.
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.
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.
Great analogy
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