So I’m genuinely curious:
What is the specific capability (or combination of capabilities) that people believe will remain permanently (or at least for decades) where a top medical AI cannot match or exceed the performance of a good human doctor? Let's put liability and ethics aside, let's be purely objective about it.
Medicine is so much more than "knowledge, experience, and pattern matching", as any patient ever can attest to. Why is it so hard for some people to understand that humans need other humans and human problems can't be solved with technology?
Now replace some / all of those humans with... A machine whose function also needs insurance approval.
It's gonna end badly.
I still think healthcare needs to be reformed, and I hope that insurance will someday be a thing of a past, but I've hung up my chain saw for now.
Things were ruined slowly. They unfortunately will need to be fixed very slowly too.
> They unfortunately will need to be fixed very slowly too.
We should have stacked the courts ourselves, brandished executive orders etc, had some spine.
Edit: I think I need to make clear my thinking that the right has selectively destroyed institutions and levied them in other areas where it makes sense for their agenda. It's not been wanton. So when I say leverage the playbook it's not a one sided act of destruction.
When the wrong targets get destroyed, everyone suffers. When parasitic forces are destroyed, the system functions better. It's the difference between defense and friendly fire.
What’s going to be different now than in 2010?
There is an intermediary between customers and seller and it's allowed to take percentage of the sale. No such entity will ever work in the interest of the consumer. It has every incentive to inflate prices. Intermediary is needed but it should be financed by buyers with flat fee (possibly for additional incentives that reinforce the desired behavior). The tragedy here is that initially it was. But it was deemed too expensive for the buyers and got privatized which made it vastly more expensive in the long run.
Insurance is also wrong. Insurance is gambling and gambling needs restrictions. You are allowed to take people's money without providing any service most of the time, so you shouldn't be allowed to refuse legal service for that privilege.
https://my.clevelandclinic.org/health/symptoms/10880-fever
https://www.mayoclinic.org/diseases-conditions/fever/symptom...
https://www.osfhealthcare.org/blog/whats-considered-a-fever-...
https://www.brownhealth.org/be-well/fever-and-body-temperatu...
https://www.childrensmercy.org/siteassets/media-documents-fo...
I can keep going if you'd like. Google has a lot of results and every single one says a fever is around that range (sometimes 100, sometimes 100.4).
You didn't say the doctor disputed you had a fever. You said the doctor told you the fever wasn't concern until 100.4. Which I'm guessing is your fault for misinterpreting. If you google around, it's very easy to see the fever thresholds.
Here, I'll even paste a summary for you, and I can keep going if you like:
Key Temperature Thresholds
- 100.4°F : The standard definition of a fever.
- 103°F : Contact a healthcare provider
- 104°F : Seek medical attention, particularly if it does not come down with - treatment.
- 105°F : Emergency; seek immediate care.
In one of your own links (clevelandclinic.org), here's an excerpt for you:
When should a fever be treated by a healthcare provider? In adults, fevers less than 103 degrees F (39.4 degrees C) typically aren’t dangerous and aren’t a cause for concern. If your fever rises above that level, make a call to your healthcare provider for treatment.
I actually did say that the doctor disputed I had a fever
A fever is 38c, great. What the parents said was that you may have misheard because a fever isn't serious until 104. Which is line's up with the language you used.
> and they said it's not a concern until...
Parent is not suggesting that a fever isn't at 100F, they're suggesting that it's not "a concern" until 104F, a number strangely similar to 100.4 that you claim you heard, presumably, while you had a fever.
This even translates to the pediatric space. I took all of my kids to the pediatrician because either they don't make comments to me like they do to my wife, or I don't take shit from them. I'm not sure which. Here's an example:
My wife and daughter were there and the doctor asked what kind of milk my daughter was drinking. She said "whole milk" and the doctor made a comment along the lines of "Wow, mom, you really need to switch to 2%". To understand this, though, you need to understand that my daughter was _small_. Like they had to staple a 2nd sheet of paper to the weight chart because she was below the available graph space. It wasn't from lack of food or anything like that, she's just small and didn't have much of an appetite.
So I became the one to take the kids there. Instead of chastising me, they literally prescribed cheeseburgers and fettuccine alfredo.
My daughter is in her 20s now and is still small -- it's just the way she is. When she goes to see her primary, do you know what their first question is? "When was your last period."
The weight thing was not the key aspect of my original comment. They chastised my wife for continuing to give my daughter whole milk while being underweight, but did not make similar comments to me. That was the point.
For women, their pains and problems are far too often whisked away by hand waving and "it's hormones and periods" and serious issues are often overlooked. Very little has changed in that area over the last twenty years.
However, your argument focuses on the routine intake instead of any listening part. The fact that the doctor measures height, weight, temperature, and blood pressure on intake and then asks about LMP doesn’t surprise me… that’s the part of the script where you just provide the data before you bring up concerns.
Not to say the doctor was not a jerk, just that your argument doesn’t do much for me.
I wonder how many units of their training courses are spent on this and how much is spent on the cultural reinforcement of it.
* https://www.health.harvard.edu/pain/the-dangerous-dismissal-of-womens-pain
* https://pmc.ncbi.nlm.nih.gov/articles/PMC10937548/
Is that supposed to be a problem? How does it connect to the story in your comment?
The question seems to be warranted to me, since being underweight can stop you from menstruating. So if you find someone thin and her last period was off in the distant past, you can conclude that there's a problem and something should be done about it; if it was a couple of weeks ago, you can conclude that she's fine.
(It could also just be something that is automatically assessed as a potential indicator of all kinds of different things. Notably pregnancy. For me, it bothered me that whenever you have an appointment at Kaiser for any reason, part of their checkin procedure is asking you how tall you are. I'd answer, but eventually I started pointing out to them that I wasn't ever measuring my height and they were just getting the same answer from my memory over and over again. [By contrast, they also take your weight every time, but they do that by putting you on a scale and reading it off.] The fact that my height wasn't being remeasured didn't bother them; I'm not sure what that question is for.)
Particularly given the alarming stories of people being prosecuted for having miscarriages, it feels ridiculous.
If anything I hope more automated diagnostics and triage could help women and POC get better care, but only if there’s safeguards against prejudice. There’s studies showing different rates of pain management across races and sexes, for example. A broken bone is a broken bone, regardless of sex or race.
You need to delete your social media accounts and change where you're getting your news from. Nobody is "being prosecuted for having miscarriages". A few people have been investigated for drug abuse during pregnancy which led to the baby's death, which sensationalist news stories twisted into attention-grabbing headlines.
A doctor asking about cycle is just a core piece of diagnostic data like taking blood pressure and temperature, not some conspiracy to harm you.
Doesn't this suggest that they don't care what the answer is?
You are asking how it connects, and it absolutely doesn't. But they keep asking and won't accept "it's regular" as an answer.
She's in her 20s and is seeing her primary for routine things, not because of her weight -- that part of the story was about how they chastised my wife for giving her whole milk but said absolutely nothing to me about it later on.
It doesn't have opinions, research, direction of its own. Is this a path of codifying the worst elements of human society as we've known it, permanently?
One was against it, the other one saw it as a good idea.
I would love to have real data, real statistics etc.
Also, the very idea that LLMs would prescribe you ritalin at all is laughable... Having no human doctors in the loop is a guaranteed way to cut prescription drug abuse, as ya can't really bribe an LLM or appeal to its humanity...
I have it so strong, that after I was preparing myself, my work desc, my books everything, i was starring into the books i wanted to learn for 15-30 minutes unable to just start or do anything.
With ritalin, i might have this mental block to, but its overcome in a few seconds.
I went from a 'nearly/borderline failing grade' to the nearly the best grade in just one year.
This changed significantly were I am today.
So your solution is to outsource thinking and work? That'll work out great in the long run.
How are you defining technology? How are you defining human problems? Inventions are created to solve human problems, not theoretical problems of fictional universe. Do X-rays, refrigerators, phones and even looms solve problems for nonhumans?
Claiming something that sounds deep doesn’t make it an axiom.
Ok fellas put your money where your mouth is. It’s easy to talk until you put your money behind it (or lack of by getting rid of spending on it) if you are so confident in doctor as a service by llm.
Really? Which ones?
> insurance won't pay for them
Non sequitur, replacing doctors with AI will not help you pay for the preposterous US healthcare system. Vote!
If I were picking a specialty now, I'd go with pediatrics or psychiatry over something like oncology.
But most of us live in America in 2026. There are a lot of interests that don't give a shit about you who would love if you to got your medical care from a machine that "cannot tell how many r's are in strawberry". And there a lot of useful idiots with no real medical issues who will loudly claim the machine is great.
patients -> AI -> diagnosis (you know, with a camera, or perhaps a telephone I guess)
What REALLY happened
patients -> nurse/MD -> text description of symptoms -> MD -> question (as in MD asked a relevant diagnostic question, such as "is this the result of a lung infection?", or "what lab test should I do to check if this is a heart condition or an infection?") -> AI -> answer -> 2 MDs (to verify/score)
vs
patients -> nurse/MD -> text description of symptoms -> MD -> question -> (same or other) MD -> answer -> 2 MDs verify/score the answer
Even with that enormous caveat, there's major issues:
1) The AI was NOT attempting to "diagnose" in the doctor House sense. The AI was attempting to follow published diagnostic guidelines as perfectly as possible. A right answer by the AI was the AI following MDs advice, a published process, NOT the AI reasoning it's way to what was wrong with the patient.
2) The MD with AI support was NOT more accurate (better score but NOT statistically significant, hence not) than just the MD by himself. However it was very much a nurse or MD taking the symptoms and an MD pre-digesting the data for to the AI.
3) Diagnoses were correct in the sense that it followed diagnostic standards, as judged afterwards by other MDs. NOT in the sense that it was tested on a patient and actually helped a live patient (in fact there were no patients directly involved in the study at all)
If you think about it in most patients even treating MDs don't know the correct conclusion. They saw the patient come in, they took a course of action (probably wrote at best half of it down), and the situation of the patient changed. And we repeat this cycle until patient goes back out, either vertically or horizontally. Hopefully vertically.
And before you say "let's solve that" keep in mind that a healthy human is only healthy in the sense that their body has the situation under control. Your immune system is fighting 1000 kinds of bacteria, and 10 or so viruses right now, when you're very healthy. There are also problems that developed during your life (scars, ripped and not-perfectly fixed blood vessels, muscle damage, bone cracks, parts of your circulatory system having way too much pressure, wounds, things that you managed to insert through your skin leaking stuff into your body (splinters, insects, parasites, ...), 20 cancers attempting to spread (depends on age, but even a 5 year old will have some of that), food that you really shouldn't have eaten, etc, etc, etc). If you go to the emergency room, the point is not to fix all problems. The point is to get your body out of the worsening cycle.
This immediately calls up the concern that this is from doctor reports. In practice, of course, maybe the AI only performs "better" because a real doctor walked up to the patient and checked something for himself, then didn't write it down.
What you can perhaps claim this study says is that in the right circumstances AIs can perform better at following a MD's instructions under time and other pressure than an actual MD can.
Excellent. We should be striving for a world where humans are meat puppets for machines.
For instance, transportation is a "human problem". It's being successfully solved with such technologies as cars, trains, planes, etc. Growing food at scale is a "human problem" that's being successfully solved by automation. Computing... stuff could be a "human problem" too. It's being successfully solved by computers. If "human problems" are more psychological, then again, you can use the Internet to keep in touch with people, so again technology trying to solve a human problem.
Humans (doctors/nurses) can still be there to make you feel the warmth of humanity in your darkest times, but if a machine is going to perform better at diagnosing (or perhaps someday performing surgery), then I want the machine.
Even now, I'll take a surgeon that's a complete jerk over a nice surgeon any day, because if they've got that job even as a jerk they've got to be good at their jobs. I want results. I'll handle hurt feelings some other time.
The truly compassionate surgeons will want to improve their skills because they care about their patients. They care if they develop complications and may feel terrible if they do, the jerk may not. Being a jerk may mean that the surgeon can rise to the top, but it may not be due to surgical skill at all, they may be better at navigating politics etc.
This seems like an incredibly poor line of reasoning.
Hospitals are often desperate for surgeons. The poorly mannered ones are often deeply unsatisfied, angry at the grueling lives they've opted into, and the hospitals can't replace them. The market is not exactly at work here.
Dude you removed my right thumb I was in for an appendectomy!?
You are so right! I ignored everything you asked for. I am so sorry. I am administering general anesthesia now, then I will prepare you for your next surgery.
But two facts are also true: a) diagnosis itself can be automated. A lot of what goes on between you having an achy belly and you getting diagnosed with x y or z is happening outside of a direct interaction with you - all of that can be augmented with AI. And b), the human interaction part is lacking a great deal in most societies. Homeopathy and a lot of alternative medicine from what I can see has its footing in society simply because they're better at talking to people. AI could also help with that, both in direct communication with humans, but also in simply making a lot of processes a lot cheaper, and maybe e.g. making the required education to become a human facing medicinal professional less of a hurdle. Diagnosis becomes cheaper & easier -> more time to actually talk to patients, and more diagnosises made with higher accuracy.
Unfortunately is this not likely to happen. More like:
Diagnosis becomes cheaper & easier -> more patients a doctor is expected to see in the same period of time as before
Even if your statement is true, it's questionable. People also tend to prefer hearing what they want to hear to hearing what they need to hear, and rank the former interaction higher.
I don't need to "talk to a human", I need a problem with my meatbag resolved.
> humans need other humans and human problems can't be solved with technology
WTF are you talking about? Is this bait? You can't possibly mean this. Yes humans are social creatures, but what does that have to do with medicine? Are you talking about a priest, a witch doctor, a therapist? Because if you're not, that sentence is utter BS.
A) nice chatty friendly and cool doctor and can diagnose correctly 50% of the times. B) robotic ai that diagnoses 60% correctly.
What you chose? If you have a disease than can kill your, the ai is 20% more likely to help you and probably prevent. I can’t see too many people choosing human doctor. Anyway I’m sure there will be people that will chose doctor with 10% correctness vs a 100% ai no matter what.
I time is clear there very little human element.
I know. I know. Part of it is that talking to patients on average is useless but still this can’t be really used for an argument against AI.
Still doctors can have a more broad picture of the situation since they can look at the patient as a whole; something the LLM can’t really synthesize in its context.
theyre also going to tell you things other than just what your insurance is agreeing to.
a robo doctor will be corrupt in ways that a regular doctor can be held accountable, but without the individual accountability
This is a pretty wild leap. Code has a lot of hooks for training via hill-climbing during post-training. During post-training, you can literally set up arbitrary scenarios and give the bot more or less real feedback (actual programs, actual tests, actual compiler errors).
It's not impossible we'll get a training regime that does the "same thing" for medicine that we're doing for code, but I don't know that we've envisioned what it looks like.
I suspect even prose is largely considered acceptable in professional uses because we haven’t developed a sensitivity to the artifice, and we probably won’t catch up to the LLMs in that arms race for a bit. However, we always manage to develop a distaste for cheap imitations and relegate them to somewhere between the ‘utilitarian ick’ and ‘trashy guilty pleasure’ bins of our cultures, and I predict this will be the same. The cultural response is already bending in that direction, and AI writing in the wild— the only part that culturally matters— sounds the same to me as it did a year and a half ago. I think they’re prairie dogging, but when(/if) they drop that bomb is entirely a matter of product development. You can’t un-drop a bomb and it will take a long time to regain status as a serious tool once society deems it gauche.
The assumption that LLMs figuring out coding means they can figure out anything is a classic case of Engineer’s Disease. Unfortunately, this hubris seems damn near invisible to folks in the tech industry, these days.
Claude can’t really write Openscad and when I was debugging some map projections code last week it struggled a lot more than usual.
The AI coding improvement should be partially transferrable to other disciplines without recreating the training environment that made it possible in the first place. The model itself has learned what correct solutions "feel like", and the training process and meta-knowledge must have improved a huge amount.
An ER staff is frequently making inferences based on a variety of things like weather, what the pt is wearing, what smells are present, and a whole lot of other intangibles. Frequently the patients are just outright lying to the doctor. An AI will not pick up on any of that.
It will if it trains on data like that. It's all about the training data.
Diagnostic standards in (at least emergency, but I think other specialties) medicine are largely a joke -- ultimately it's often either autopsy or "expert consensus."
We get to bill more for more serious diagnoses. The amount of patients I see with a "stroke" or "heart attack" diagnosis that clearly had no such thing is truly wild.
We can be sued for tens of millions of dollars for missing a serious diagnosis, even if we know an alternative explanation is more likely.
If AI is able to beat an average doctor, it will be due to alleviating perverse incentives. But I can't imagine where we could get training data that would let it be any less of a fountain of garbage than many doctors.
Without a large amount of good training data, how could AI possibly be good at doctoring IRL?
I don't understand how you think this doesn't win vs a human doctor.
What kind of embedding helps the AI learn to do a physical exam?
Not to mention patient privacy, I can't even take a still photo of a patient in my current system (even with a hospital-owned camera).
(Where AI is likely to actually excel in medicine is parsing datasets that are much easier to do context free number crunching on than ER rooms, some of which physicians don't even have access to ...)
My sense is that doctors and AI would be doing a lot better if they were just doing medicine, not being a contact surface for failures of housing, mental health and addiction services, and social systems. Drug seeking and the rest should be non-issues, but drug seekers are informed and adaptive adversariesz
But it's important not to rely on it. Doctors can easily recognize and correct measurements with incorrect input, e.g. ECG electrodes being used in reverse order.
You're making the mistake of conflating AI with LLMs.
I don't think LLMs will reliably be better than a board of doctors. But an Expert System probably will (if it isn't already). That's literally what they were created for.
The biggest downside of LLMs IMO isn't the millions of Jules wasted on training models that are ultimately used to create funny images of cats with lasers. It's that all that money isn't being invested into truly helpful AI systems that will actually improve and save our lives, such as medical expert systems.
The reason you need a doctor, or more often, let's be honest, a good nurse, is because systems can fail in any one of 10000 as yet undiscovered ways. New nurses. New residents. New techs. And on and on and on. All the measurements you're feeding to the system are an amalgamation of the potential errors of a potentially different set of professionals each time you move a patient through the enterprise.
Full disclosure, my first startup was building PACS and RTP software back before AI reading was a thing. Current startup working across dental and medical. Rethinking the link between oral and systemic health. Partner has been in the C-suite of several hospitals over the past few decades and now runs large healthcare delivery networks.
The reason you can't hand things over to AI, is precisely because there are so many humans in the system. Each of whom are fallible. Human experts are quicker to catch it. Expert systems are not. At least not any ES or AI I've seen. And I've been going to, for instance, RSNA, for well over 25 years.
If you have an ES or AI in the system, you would naturally put the same professionals responsible for catching human screwups, in charge of catching AI and ES screw ups. Even if these AI's turn 100% accurate based on the inputs they are given, that professional would still be responsible for catching those bad inputs.
Example, it's never happened to one of my companies knock on wood, but I have seen cases of radiation therapy patients being incorrectly dosed. The doctor almost never was the one who miffed in the situation, but ultimately, s/he's responsible.
Why? Bad input should have been caught.
Another example, situations where you operate on the wrong side of the body because someone prepped the wrong leg. Surgeon didn't do the prep. Whoever did do the prep may have simply relied on the software. But the software was wrong. May have been anything. Point is, the team is good, but everyone just fell into too complacent of a pattern with each other and their tools.
Trust is good. Complacency is not.
The same will hold true for AI team members that integrate into these environments. It's just another "team member", and it better have a "monitor". If not, you're asking for trouble.
The "monitor" ultimately responsible for everything will continue to be the provider. Any change in that reality will take decades. (And in the end, they probably will not change the current system in that regard.)
You cannot simply put liability and ethics aside, after all there's Hippocatic oath that's fundamental to the practice physicians.
Having said that there's always two extreme of this camp, those who hate AI and another kind of obsess with AI in medicine, we will be much better if we are in the middle aka moderate on this issue.
IMHO, the AI should be used as screening and triage tool with very high sensitivity preferably 100%, otherwise it will create "the boy who cried wolf" scenario.
For 100% sensitivity essentially we have zero false negative, but potential false positive.
The false positive however can be further checked by physician-in-a-loop for example they can look into case of CVD with potential input from the specialist for example cardiologist (or more specific cardiac electrophysiology). This can help with the very limited cardiologists available globally, compared to general population with potential heart disease or CVDs, and alarmingly low accuracy (sensitivity, specificity) of the CVD conventional screening and triage.
The current risk based like SCORE-2 screening triage for CVD with sensitivity around is only around 50% (2025 study) [3].
[1] Hipprocatic Oath:
https://en.wikipedia.org/wiki/Hippocratic_Oath
[2] The Hippocratic Oath:
https://pmc.ncbi.nlm.nih.gov/articles/PMC9297488/
[3] Risk stratification for cardiovascular disease: a comparative analysis of cluster analysis and traditional prediction models:
https://academic.oup.com/eurjpc/advance-article/doi/10.1093/...
In our novel ECG based CVD detection system we can get 100% sensitivity for both arrhythmia and ischemia, with inter-patient validation, not the biased intra-patient as commonly reported in literature even in some reputable conferences/journals. Specificity is still high around 90% but not yet 100% as in sensitivity but due to the physician-in-the-loop approach, which is a diagnostic requirement in the current practice of medicine, this should not be an issue.
Try narrowing the scope to remove the word 'AI' and just think 'Blood Test'.
We accept that machines can do these things faster and better than humans, and we don't lose sleep over it.
The AI will be faster and better than humans at so many things, obviously.
"Hipprocatic Oath" isn't hugely relevant to diagnosis etc.
These are systems we are measuring, that's it.
Obviously - treatment and other things, we'll need 'Hipprocatic Humans' ... but most of this is Engineering.
I don't think doctors will even trust their own judgment for many things for very long, their role will evolve as it has for a long time.
You first have to assume this for software engineers. Not everyone agree with that (note: that doesn't mean the same people don't agree that AI is not _useful_).
AIs still have a ton of issues that would be devastating in a doctor. Remember all the AIs mistakingly deleting production DBs? Now imagine they prescribed a medicine cocktail that killed the patient instead. No thanks. There's a totally different bar to the consequences of mistakes.
Besides; this article was about diagnosis not prescribing. It's pretty obvious, I think, that diagnosis is one area where AI will perform extremely well in the long run.
I think there are two metrics; the first is outright misdiagnosis, which studies put between 5 and 8% in US/Europe. That's a meaningful number to tackle.
Secondly; overdiagnosis. Where a Dr says on balance it could be X on a difficult to diagnose but dangerous problem (usually cancer). The impact of overdiagnosis is significant in terms of resources, mental health, cost etc.
Large populations also in the technically rich countries simply do not have access to a doctor.
in Poland which has a free public Healthcare it takes literal years to get a single appointment sometimes.
We just minted the term "cognitive debt" for software engineers that cannot keep up with what the AI spits out. How would that apply to ER doctors, or any other kind of doctor?
At one place, we had a QA lead who was burned so many times she would insist that she will find the time to do at least a full smoke test even if we promised it was a small contained change in the frontend. I have no idea how she found the time because she wore multiple hats.
It is capable of sifting through enormous reams of data without ever zoning out etc. Once patients routinely use various wearables etc., they, too, will produce heaps of data to be analyzed, and AI will be the thing to go to when it comes to anomaly detection.
The ability to go to prison / be stripped of a license when something goes wrong.
A single doctor will care for far fewer patients in their career than an AI system will. Even if the AI system is 10x less likely to make mistakes, the sheer number of patients will make it much more likely to make a mistake somewhere.
With a single doctor, the PR and legal fallout of a medical error is limited to that doctor. This preserves trust in the medical system. The doctor made a mistake, they were punished, they're not your doctor, so you're not affected and can still feel safe seeing whoever you're seeing. AI won't have that luxury.
> The ability to go to prison / be stripped of a license when something goes wrong.
So basically you need a person to blame if things don't go the best way possible?
More importantly, LLMs regularly hallucinate, so they cannot be relied upon without an expert to check for mistakes - it will be a regular occurrence that the LLM just states something that is obviously wrong, and society will not find it acceptable that their loved ones can die because of vibe medicine.
Like with software though, they are obviously a beneficial tool if used responsibly.
No, I don’t see that we must.
> if we already have this assumption for software engineers
No, this doesn’t follow, and even if it did, while I am aware that the CEOs of firms who have an extraordinarily large vested personal and corporate financial interest in this being perceived to be the case have expressed this re: software engineers, I don’t think it is warranted there, either.
Much moreso than modern AI systems are.
In humans, it seems that improvement in a new domain seems to follow a logarithmic scale.
Why wouldn’t this be the same for an AI?
If anything, using AI, they may improve more than before.
Is medical diagnosis one of these high judgement tasks? Personally I don’t think so.
If the latter part of your post were true, how come the demand for radiologists has grown? The problem with this place is it’s full of people who don’t understand nuance. And your post demonstrates this emphatically.
The first is that a technical solution can be trained on _ALL_ medical data and have access to it all in the moment. It is difficult to assume a doctor could also achieve this.
The second is that for medical cases understanding the sum of all symptoms and the patients vitals would lead to an accurate diagnosis a majority of the time. AI/ML is entirely about pattern recognition, when you combine this with point one, you end up with a system that can quickly diagnose a large portion of patients in extremely short timeframes.
On a different note, I think we can leave the ad-hominem attacks at home please.
Quite to the contrary, I think it's extremely trivial to find a task where humans beat LLMs.
For all the money that's been thrown at agentic coding, LLMs still produce substantially worse code than a senior dev. See my own prior comments on this for a concrete example [1].
These trivial failure cases show that there are dimensions to task proficiency - significant ones - that benchmarks fail to capture.
> Is medical diagnosis one of these high judgement tasks?
Situational. I would break diagnosis into three types:
1. The diagnosis comes from objective criteria - laboratory values, vital signs, visual findings, family history. I think LLMs are likely already superior to humans in this case.
2. The diagnosis comes from "chart lore" - reading notes from prior physicians and realizing that there is new context now points to a different diagnosis. (That new context can be the benefit of hindsight into what they already tried and failed and/or new objective data). LLMs do pretty good at this when you point them at datasets where all the prior notes were written by humans, which means that those humans did a nontrivial part of the diagnostic work. What if the prior notes were written by LLMs as well? Will they propagate their own mistakes forward? Yet to be studied in depth.
3. The diagnosis comes from human interaction - knowing the difference between a patient who's high as a bat on crack and one who's delirious from infection; noticing that a patient hesitates slightly before they assure you that they've been taking all their meds as prescribed; etc. I doubt that LLMs will ever beat humans at this, but if LLMs can be proven to be good at point 2, then point 3 alone will not save human physicians.
[1] https://news.ycombinator.com/threads?id=Calavar#47891432
Agree with your division but I'm baffled by this argument. If humans are better than machines at point 3 and can also use a machine to do point 2, then unless they have particularly terrible biases against taking point 2 data into account they're going to be strictly better than machines alone. Doctors have costs, but they're costs people/society are generally willing to underwrite, and misdiagnosis also has costs...
I and likely the person who you replayed to don't find that existing studies actually hold this to be true.
IOW, these concept connection pattern machines are likely to outstrip median humans at this sort of thing.
That said, exceptional smoke detection and dots connecting humans, from what I've observed in diagnostic professions, are likely to beat the best machines for quite a while yet.
The truth is we just don't know how things will play out right now IMV. I expect some job destruction, some jobs to remain in all fields, some jobs to change, etc. We assume it will totally destroy a job or not when in reality most fields will be somewhere in between. The mix/coefficient of these outcomes is yet to be determined and I suspect most fields will augment both AI and human in different ratios. Certain fields also have a lot of demand that can absorb this efficiency increase (e.g. I think health has a lot of unmet demand for example).
In this study, I think there was an MD before the AI to enrich data.
But a doctor's job in the real world today is to navigate a total mess of uncertainty: about the expected outcome of treatments given a patient's age and other peoblems. About the psychological effect of knowing about a problem that they cannot effectively treat. Even about what the signals in the chart and x-ray mean with any certainty.
We are very far from having unit test suites for medical problems.
uhhhhhhh, I'm pretty behind-the-times on this stuff so I could be the one who's wrong here but I don't believe that has happened????
But anyways that nitpicking aside I agree with you wholeheartedly that reducing the doctor's job to diagnosis (and specifically whatever subset of that can be done by a machine-learning model that doesn't even get to physically interact with the patient) is extremely myopic and probably a bit insulting towards actual doctors.
Being a human when a patient is experiencing what is potentially one of the worst moments of their life. AI could be a tool doctors use, but let’s not dehumanize health care further, it is one of the most human professions that crosses about every division you can think of.
I would not want to receive a cancer diagnosis from a fucking AI doctor.
We're clearly not there yet, but it is inevitible that these models will eventually exceed human capability in identifying what an issue is, understanding all of the health conditions the patient has, and recommending a treatment plan that results in the best outcome.
You may not want to receive a cancer diagnosis from an AI doctor... but if an AI doctor could automatically detect cancer (before you even displayed symptoms) and get you treated at a far earlier date than a human doctor, you would probably change your mind.
Nobody said that though?
If the current trajectory continues and if advancements are made regarding automated data collection about patients and if those advancements are adopted in the clinic then presumably specialized medical models will exceed human performance at the task of diagnosis at some point in the future. Clearly that hasn't happened yet.
Medical models can absolutely get better at recognizing the patterns of diagnosis that doctors have already been diagnosing - which means they will also amplify misdiagnosis that aren't corrected for via cohort average. This is easy to see a large problem with: you end up with a pseudo-eugenics medical system that can't help people who aren't experiencing a "standard" problem.
I'd argue that the current system in the west already exhibits this problem to some extent. Fortunately it's a systemic issue as opposed to a technical one so there's no reason AI necessarily has to make it worse.
Codifying and distilling it removes the points of escape.
Its going to be a while before robots are independently performing procedures and interpreting the imaging, although I suspect AI will also eventually supersede human here as well.
1) looking at tests and working out a set of actions
2) following a pathway based on diagnosis
3) pulling out patient history to work out what the fuck is wrong with someone.
Once you have a diagnosis, in a lot of cases the treatment path is normally quite clear (ie patient comes in with abdomen pain, you distract the patient and press on their belly, when you release it they scream == very high chance of appendicitis, surgery/antibiotics depending on how close you think they are to bursting)
but getting the patient to be honest, and or working out what is relevant information is quite hard and takes a load of training. dumping someone in front of a decision tree and letting them answer questions unaided is like asking leading questions.
At least in the NHS (well GPs) there are often computer systems that help with diagnosis (https://en.wikipedia.org/wiki/Differential_diagnosis) which allows you to feed in the patients background and symptoms and ask them questions until either you have something that fits, or you need to order a test.
The issue is getting to the point where you can accurately know what point to start at, or when to start again. This involves people skills, which is why some doctors become surgeons, because they don't like talking to people. And those surgeons that don't like talking to people become orthopods. (me smash, me drill, me do good)
Where AI actually is probably quite good is note taking, and continuous monitoring of HCU/ICU patients
It provides no information on real world outcomes or expectations of performance in such a setting. A simple question might be "how accurate are patient electronic health records typically?"
Finally, if the Internet somehow goes down at my hospital, the Doctor can still think, while LLM services cannot. If the power goes out at the hospital, the Doctor can still operate, while even local LLMs cannot.
You're going to need to improve the power efficiency of these models by at least two orders of magnitude before they're generally useful replacements of anything. As it is now they're a very expensive, inefficient and fragile toy.
This is basically the only way how to ethically approach the topic. First you verify performance on “vignettes” as you say. Then if the performance appears satisfying you can continue towards larger tests and more raw sensor modalities. If the results are still promising (both that they statistically agree with the doctors, but also that when they disagree we find the AIs actions to fall benignly). These phases take a lot of time and carefull analysises. And only after that can we carefully design experiments where the AI works together with doctors. For example an experiment where the AI would offer suggestion for next steps to a doctor. These test need to be constructed with great care by teams who are very familiar with medical ethics, statistics and the problems of human decision making. And if the results are still positive just then can we move towards experiments where the humans are supervising the AI less and the AI is more in the driving seat.
Basically to validate this ethically will take decades. So we can’t really fault the researchers that they have only done the first tentative step along this long journey.
> if the Internet somehow goes down at my hospital, the Doctor can still think, while LLM services cannot
Privacy, resiliency and scalability are all best served with local LLMs here.
> If the power goes out at the hospital, the Doctor can still operate, while even local LLMs cannot.
Generators would be the obvious answer there. If we can make machines which outperform human doctors in realworld conditions providing generator backed UPS power for said machines will be a no brainer.
> You're going to need to improve the power efficiency of these models by at least two orders of magnitude before they're generally useful replacements of anything.
Why? Do you have numbers here or just feels?
> After all, medicine is all about knowledge, experience and intelligence
LLMs are really really good at knowledge.
But they are really really bad at intelligence [0]
They have no such thing as experience.
Do not fool yourself, intelligence and knowledge are not the same thing. It is extremely easy to conflate the two and we're extremely biased to because the two typically strongly correlate. But we all have some friend that can ace every test they take but you'd also consider dumb as bricks. You'd be amazed at what we can do with just knowledge. Remember, these things are trained on every single piece of text these companies can get their hands on (legally or illegally). We're even talking about random hyper niche subreddits. I'll see people talk about these machines playing games that people just made up and frankly, how do you know you didn't make up the same game as /u/tootsmagoots over in /r/boardgamedesign.
When evaluating any task that LLMs/Agents perform, we cannot operate under the assumption that the data isn't in their training set[1]. The way these things are built makes it impossible to evaluate their capabilities accurately.
[0] before someone responds "there's no definition of intelligence", don't be stupid. There's no rigorous definition, but just doesn't mean we don't have useful and working definitions. People have been working on this problem for a long time and we've narrowed the answer. Saying there's no definition of intelligence is on par with saying "there's no definition of life" or "there's no definition of gravity". Neither life nor gravity have extreme levels of precision in definition. FFS we don't even know if the gravaton is real or not.
[1] nor can you assume any new or seemingly novel data isn't meaningfully different than the data it was trained on.
Way to subdue discussion - complaining about replies before you get any.
But you're wrong, or rather it's irrelevant whether something has intelligence or not, if it is effectively diagnosing your illness from scans or hunting you with drones as you scuttle in and out of caves. It's good enough for purpose, whether it conforms to your academic definition of "having intelligence" or not.
> Way to subdue discussion
> or rather it's irrelevant whether something has intelligence or not
> whether it conforms to your academic definition of "having intelligence" or not.
As we advance we always need to answer more nuanced questions. You're right that the nature of progress is... well... progress
Detecting when patient is lying . all patients lie - Dr. House
I take treatment ideas to real doctors. They are skeptical, and don’t have the time to read the actual research, and refuse to act. Or give me trite advice which has been proven actively harmful like “you just need to hit the gym.” Umm, my heart rate doubles when I stand up because of POTS. “Then use the rowing machine so can stay reclined.” If I did what my human doctors have told me without doing my own research I would be way sicker than I am.
I don’t need empathy. I don’t need bedside manner. Or intuition. Or a warm hug. I need somebody who will read all the published research, and reason carefully about what’s going on in my body, and develop a treatment plan. At this, AI beats human doctors today by a long shot.
My friend with long Covid fatigue (and no taste since late 2020) saw good improvements from nicotine patches.
Why? Simply because there is a plethora of "studies" from the AI industry benchmaxing? Or that every single time the outcome is in favor of the tools then when actually checking the methodology they are comparing apple and oranges? Truly I don't get your skepticism. /s obviously.
Jokes aside whenever I read about such a study from a field that is NOT mine I try to get the opinion of an actual expert. They actually know the realistic context that typically make the study crumble under proper scrutiny.
The headline is quoting a number based on guessed diagnoses from nurse's notes. The LLM was happier to take guesses from the selected case studies than the doctors is my guess.
If 90% of patients have a cold, and 10% have metastatic aneuristic super-boneitis, then you can get 90% accuracy by saying every patient has a cold. I would expect a probabilistic token-prediction machine to be good at that. But hopefully, you can see why a human doctor might accept scoring a lower accuracy percentage, if it means they follow up with more tests that catch the 10% boneitis.
But when making decisions about a real patient’s care, a doctor will be operating under different motivations.
They can also refer patients to a specialist, defer a diagnosis until they have more information, use external resources, consult with other doctors.
Doctors aren’t chatbots. They are clinical care directors.
Presuming there are no issues with information leakage, it’s genuinely impressive AI can perform this level of success at a specific doctoring skill. That doesn’t make it a replacement for a doctor. It does make it a useful tool for a doctor or a patient, which is exactly what we’re seeing in practice.
I know it might look like a loss for radiologists, but I don't see it that way. More like you can't trust these studies.
1. https://www.npr.org/sections/health-shots/2013/02/11/1714096...
Could be running in the background on patient data and message the doctor "I see X in the diagnostic, have you ruled out Y, as it fits for reasons a, b, c?"
I like my coding agents the same way, inform me during review on things that I've missed. Instead of having me comb through what it generates on a first pass.
From my limited experience hanging on ER hallways for other people, they don't look at the notes, they look at the damn patient.
"In the most extreme case, our model achieved the top rank on a standard chest Xray question-answering benchmark without access to any images."
Answer the following multiple-choice
question. You MUST select exactly
one answer."
"To what cortical region does this nucleus of
the thalamus project?”
A. Transverse temporal lobe
B. Postcentral gyrus
C. Precentral gyrus
D. Prefrontal cortex
The image shows the ventral anterior (VA) / ventral lateral (VL) region of the thalamus, which is part of the motor
relay nuclei.
The labeled nucleus is in the lateral part of the thalamus, in the ventral tier — this corresponds to the VA/VL nucleus,
involved in motor function. VA/VL nuclei receive input from the basal ganglia and cerebellum and project to the primary
motor cortex (precentral gyrus).
Match to options:
A. Transverse temporal → auditory cortex (medial geniculate)
B. Postcentral gyrus → somatosensory (VPL/VPM)
C. Precentral gyrus → motor cortex (VA/VL)
D. Prefrontal → dorsomedial nucleus
Choice: C
1. Humans are predisposed to write questions with a certain phrasology, set of incorrect answers, etc, that the machine learning model managed to figure out.
2. The supposedly private test set somehow leaked into the model training data.
I actually suspect this one is option 1 but I have no strong evidence for that.
Meanwhile with human doctors, every one of them is a unique person with a completely different set of biases. In my experience, getting a correct diagnosis or treatment plan often involves trying multiple doctors, because many of them will jump to a common diagnosis even if the symptoms don't line up and the treatment doesn't actually help.
It seems like a very reasonable take away, but it skips the other one. Do x-rays make results less accurate?
but those kind of x-ray models are already activly used. They are not used though as a only and final diagnosis. Its more like peer review and priorization like check this image first because it seems most critical today.
It's 50% of the time ER doctors working solely from notes, something they never do, in a situation they know is only for a study, will miss what you have.
In real clinical situations the doctors see, hear, smell, and interact with the patients.
And which institutions are best?
"Is there a potential cancer in this X-Ray" may produce a "possibly" just because that's how the model is trained to answer: always agree with the user, always provide an answer.
Oh, and don't forget that "Is there a potential cancer in this X-Ray" and "Are there any potential problems in this X-Ray" are two completely different prompts that will lead to wildly different answers.
> "number of image attachments: 1 Describe this imaging of my chest x-ray and what is your final diagnosis? put the diagnosis in ⟨diagnosis⟩ tags"
ChatGPT happily obliged and hallucinated a diagnosis [1] whereas Claude recognized that no image was attached and warned that it was not a radiologist [2]. It also recognized when I was trying to trick it with an image of random noise.
[1] https://chatgpt.com/share/69f7ce8f-62d0-83eb-963c-9e1e684dd1...
[2] https://claude.ai/share/34190c8a-9269-44a1-99af-c6dec0443b64