See for example this recent paper where AI managed to beat radiologists on interpreting x-rays... when the AI didn't even have access to the x-rays: https://arxiv.org/pdf/2603.21687 (on a pre existing "large scale visual question answering benchmark for generalist chest x-ray understanding" that wasn't intentionally messed up).
And in interpreting x-ray's human radiologists actually do just look at the x-rays. In the context the article is discussing the human doctors don't just look at the notes to diagnose the ER patient. You're asking them to perform a task that isn't necessary, that they aren't experienced in, or trained in, and then saying "the AI outperforms them". Even if the notes aren't accidentally giving away the answer through some weird side channel, that's not that surprising.
Which isn't to say that I think the study is either definitely wrong, or intentionally deceptive. Just that I wouldn't draw strong conclusions from a single study here.
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?
One was against it, the other one saw it as a good idea.
I would love to have real data, real statistics etc.
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.
If I were picking a specialty now, I'd go with pediatrics or psychiatry over something like oncology.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/...
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.
In this study, I think there was an MD before the AI to enrich data.
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.
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.
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.
Is medical diagnosis one of these high judgement tasks? Personally I don’t think so.
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).
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.
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.
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.
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.
"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.
From my limited experience hanging on ER hallways for other people, they don't look at the notes, they look at the damn patient.
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.
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.
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'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.
"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
I think it's important to note that diagnosis also relies on accurate description of the patient in the first place, and the information you gather depends on the differential diagnosis. Part of the skill of being a doctor is gathering information from lots of different sources, and trying to filter out what is important. This may be from the patient, who may not be able to communicate clearly or may be non verbal, carers and next of kin. History-taking is a skill in itself, as well as examination. Here those data are given.
For pattern recognition from plain text, especially on questions that may be in the o1's training data, I'm not surprised at all that it would outperform doctors, but it doesn't seem to be a clinically useful comparison. Deciding which investigations to do, any imaging, and filtering out unnecessary information from the history is a skill in itself, and can't really be separated from forming the diagnosis.
Simply getting the "high score" on this evaluation is not necessarily good medical treatment.
Even as an AI-neutral person, I'm very confident that AI/ML based computer systems, once trained specifically for medicine, will consistently do better than human doctors because believe it or not, there are a lot of human errors made in medicine field (doctors just don't admit that and we don't know) due to lack of time by doctors or incompetence or simply forgetting a fact or two that they should have checked when diagnosing or coming up with a treatment.
This is handicapping the human doctors abilities. There is a lot more information a human doctor can gather even with a brief observation of the patient.
> there are few things as dangerous as an expert with access to open-ended data that can be interpreted wildly, like a clinical interview.
https://entropicthoughts.com/arithmetic-models-better-than-y...
> But it is not curtains for emergency doctors yet, the researchers said. The study only tested humans against AIs looking at patient data that can be communicated via text. The AI’s reading of signals, such as the patient’s level of distress and their visual appearance, were not tested. That means the AI was performing more like a clinician producing a second opinion based on paperwork.
That actually seems like a good application – automatically get a quick AI second opinion for everything; if it's dissenting the first/human medic can re-review, or comment why it's slop, or get a third/second-human opinion.
(I'm assuming most cases would be You're absolutely right, that's an astute diagnosis.)
In this regard. A doctor also just have 15 minutes for an interview. An Ai can be with the patient for days leading up to a consultation.
So if we remove this "handicap" this Ai will likely really start to win.
The other thing is that common issues are common. I have to wonder how much that ultimately biases both the doctor and the LLM. If you diagnose someone that comes in with a runny nose and cough as having the flu you will likely be right most of the time.
This one compares AI to a human doctor practicing in a very unrealistic way.
When I got tired of this I just lied to the emergency line and was admitted to hospital based on my lie, and they discovered a brain tumor which explained the other stuff.
I WISH I could just use AI.
Now feed a flawed transcripted into an AI diagnosis system and bam-o. The AI will treat it as gospel, while the doctor may go wait what.
Also, later in the encounter, with more chart information, AI scored 82%, physicians 70–79%; that difference was reportedly not statistically significant.
So current AI can aid in diagnosing like we've all known.
Case in point, I went to a podiatrist for foot and ankle issues. He diagnosed my foot issues from the xray but just shrugged his shoulders for the ankle issues and said the xray didn't show anything. My 15 minute allocation of his attention expired and I left without a clue as to the issue or what corrective actions to take. 5 minutes with an LLM and I had a plausible reason for the ankle issues which aligned with the diagnosis in my foot.
Real doctors tend to have a degree of cautiousness. I would rather a real doctor be hesitate and seek more information, than an alarmist LLM suggesting I have cancer.
Unless healthcare businesses decide to improve patient care with AI instead of increasing patients per day, I think it's going to make things even worse.
I have no way of knowing if this is true. But I‘d rather had a complete, guided prompt be the basis of a diagnosis, than a 2m google search.
I think AI, like in all other fields, will become a great tool to help augment. Throw the patient data in and get a response and that can be the first thing the doctor checks for, but they shouldn't simply take AI as truth.
P.S. friends kid is doing great - it was caught early enough. They are due to be completely done with treatment in just a couple months!
I was pretty freaked out. During that time, I tried diagnosing it with AI. When I finally got to the appointment, the actual doctor sat down, looked at all the unremarkable images, asked me one (1) question, ordered another image and diagnosed the issue. When I looked back, in all that time, the AI had mentioned it exactly one time early on, ruled it out immediately based on a flawed understanding of the symptoms, and never brought it up again.
Just my anecdotal evidence, but I’d never trust any AI on its own. My doctor can use it if they want, I can’t.
(I was ~3 months away from wheelchair bound in those x-rays).
The worst one was Gemini. Upload an x-ray of just the right hip, and it started to talk about how good the left hip looked like.
I think with AI taking over it's gonna be harder to get a solution when your problem isn't the run-of-the mill.
But specialized models can be inhumanly good. I know, our main product is a model that does _precise_ analysis :)
My philosophical take: if AI can outperform the average, it’s probably a net benefit for society that I won’t have a job. Until then, I’m going to take my income and save up for an early retirement.
I am very skeptical of studies like this that don't adequately reflect real world conditions, but when I was a software engineer I probably wouldn't have understood what "real" medicine is like either.
Our findings found that gpt-5-mini performed better than gpt-5, sonnet 4 and medgemma.
I think these studies are very hard to accurately score. But in any case, AI seems to do a very good job compared to humans. Unsurprising, really.
How much it can be effective for science if it is not compared side by side how each scenario was evaluated by both and how it came to different conclusions.
Who can ensure a doctor couldn't spot some blind point AI couldn't at the remaining 43%.
Tools are not for replacement but combining efforts.
Throw such % to the public is a lot of irresponsibility.
I still want humans in the loop, interpreting the LLMs findings and providing a sanity check.
You can’t hold an LLM accountable.
That’s the min responsible bar for LLM authored code, which normally doesn’t really matter much. For something as important as ER diagnostics, having a human in the loop is crucial.
The narrative that these tools are replacing human intelligence rather than augmenting it is, quite frankly, stupid.
We should embrace these tools.
But, “eliminating DRs”… hardly.
If we trust machines to much...
An AI and a pair of human doctors were each given the same standard electronic health record to read – typically including vital sign data, demographic information and a few sentences from a nurse about why the patient was there. The AI identified the exact or very close diagnosis in 67% of cases, beating the human doctors, who were right only 50%-55% of the time.... The study only tested humans against AIs looking at patient data that can be communicated via text. The AI’s reading of signals, such as the patient’s level of distress and their visual appearance, were not tested. That means the AI was performing more like a clinician producing a second opinion based on paperwork.
1. AI gets data about the patient and makes a diagnosis. This is NOT shown to doctor yet.
2. Doctor does their stuff, writes down their diagnosis. This diagnosis is locked down and versioned.
3. Doctor sees AI's diagnosis
4. Doctor can adjust their diagnosis, BUT the original stays in the system.
This way the AI stays as the assistant and won't affect the doctor's decision, but they can change their mind after getting the extra data.
6. Rankings are used to periodically "trim the fact" thus delivering more optimized cash flows to clinics that have been saddled with toxic debt
7. Sensing an opportunity AI providers start selling a $200 / month Data Leakage as a Service subscription to overworked physicians so that they can avoid the PE guillotine
I agree with GP's solution but we'd need regulation to prohibit what you describe.
Incompetent ones order unnecessary tests and exhaust treatment possibilities, which drives up cost billed to insurance.
Only the insurance industry and perhaps licensing bodies can pressure to keep the quality floor high, at least in terms of accurate diagnosis and prevention of overtreatment.
It's trivial to analyse the pre/post AI involvement doctor diagnosis manually and see what's going on.
If a doctor is just putting "asdljasdaskjd" on the initial to unlock the AI answer, they should be promptly fired.
They need to write down their (initial) diagnosis before the AI answer is shown.
Even if AI is used to sample or summarize a lot of data that a human couldn't do in time: What if it misses something that a human won't? What if a human inversely misses something that AI won't? Would you rather trust the machine or the human? (Especially if the human is held accountable.)
The number in the headline isn’t even a good comparison because they asked doctors to make a diagnosis from notes a nurse typed up. Doctors are trained to be conservative with diagnosing from someone else’s notes because it’s their job to ask the patient questions and evaluate the situation, whereas an LLM will happily leap to a conclusion and deliver it with high confidence
When they allowed both humans and doctors access to more information about the case, the difference between groups collapsed into statistical insignificance:
> The diagnosis accuracy of the AI – OpenAI’s o1 reasoning model – rose to 82% when more detail was available, compared with the 70-79% accuracy achieved by the expert humans, though this difference was not statistically significant.
Talking to my medical professional friends, LLMs are becoming a supercharged version of Dr. Google and WebMD that fueled a lot of bad patient self-diagnoses in the past. Now patients are using LLMs to try to diagnose themselves and doing it in a way where they start to learn how to lead the LLM to the diagnosis they want, which they can do for a hundred rounds at home before presenting to the doctor and reciting the script and symptoms that worked best to convince the LLM they had a certain condition.
I thought websites have to make it as easy to give consent as withdraw consent[1] - and here one cannot withdraw consent without an extra step (subscribing).
Instead I would expect access to the article, with same ads as in the “user consented” path, just not personalized.
[1]: “The GDPR is specific that consent must be as 'easy to withdraw as to give'”, https://en.wikipedia.org/wiki/HTTP_cookie
If you, like me, are in the software field, know that this is likely the most comfortable job even invented by humanity, we should really be paid just above the poverty line in exchange.
However many others in society save lives that are not so lavishly praised or financially rewarded.
For example in New Zealand median pay for a Road Design Engineer is about $100k NZD compared to a GP (doctor) getting $240k. Plus the doctor gets paid a massive overpayment of social status.
Over a 40-year career, an average NZ GP will save 5 to 10 lives. The Road Design Engineer saves 40 to 120 lives. Road engineers in NZ prevent roughly 10x more serious injuries than they do deaths so it isn't just death stats.
Our hypothetical engineer should be paid > 10x more than the doctor on raw stats.
It gets harder when we start looking at quality of life versus raw lifetime numbers. You then need to consider the value of say entertainment (a good movie) versus the hypothtical lives saved by spending the budget elsewhere.
A game designer might be valued highly by a gamer mum, and negatively by their children and gaming widowed dad.
I had to leave my job this year because of burnout when the execs mandated that we use AI tools, become our own designers, PMs, and QA, and double our velocity. They run through a decision tree they leaned in residency every day and I’m learning how to do 3-4 other people’s jobs on top of whatever the new AI thing is. I was working nights and weekends while my friends in medicine are planning their 3rd vacation this year to Tuscany.
Fifty percent accuracy. That's terrible.
My wife was recently diagnosed with Mast Cell Activation Syndrome (MCAS) after a pretty scary series of ER visits. It's a very strange and stubborn autoimmune disease that manifests with a number of symptoms that, taken individually, could indicate damn near anything.
You could almost feel the doctors rolling their eyes as she explained her symptoms and medical history.
Anyway... it lit a bit of a fire in me to dig deeper, and one day Claude suggested MCAS. I started plugging in more labs, asking for Claude to cross-reference journals mentioning MCAS, and sure enough: it's MCAS.
idk what the moral of the story is except our current medical system is a joke. The doctors aren't the villains, but they sure aren't the heroes either.
Of course, there are plenty of places on earth that are extremely under doctored, and AI will definitely be better than nothing in poor regions of Africa if all it needs is a network connection and someone to donate the tokens.
While I’m sure there can be ways in which such studies are wrong, it’s very obvious that AI can accelerate work in many of these areas where we seek out professional help - doctors, lawyers, etc.
If you have string of issues with 10 last doctors though, then issue is, most probably, you...
My wife is a GP, and easily 1/3 of her patients have also some minor-but-visible mental issue. 1-2 out of 10 scale. Makes them still functional in society but... often very hard to be around with.
That doesn't mean I don't trust your words, there are tons of people with either rare issues or even fairly common ones but manifesting in non-standard way (or mixed with some other issue). These folks suffer a lot to find a doctor who doesn't bunch them up in some general state with generic treatment. There are those, but not that often.
It helps both sides tremendously if patient is not above or arrogant know-it-all waving with chatgpt into doctor's face and basically just coming for prescription after self-diagnosis. Then, help is sometimes proportional to situation and lawful obligations.
I admittedly I have a bunch of medical issues and these gems are my favourites from the GPs.
1. I cannot see the tonsil on the left side, so it is OK. (there was a 6cm!!! cyst in front of it)
2. After missing sky high TSH measures consistently for 2 years (4 testst) : "It must have been a few one offs" (no it wasn't and it is not even possible)
3. "Blood pressure has nothing to do with weight"
These %#£&* so called medical professionals are still working and most likely killing people legally.
These days I research and read studies, arm myself with knowledge, cross check with multiple LLMs and go in with a diagnosis and request a specific prescription. After 5 years with my health in the gutter I had my first comprehensive private blood test coming back with no issues.
So no, do not try to call me arrogant. I am not arrogant, I am defending myself from these "GPs" so they won't put me in an early grave by making fatal mistakes.
Doctors thinking patients are arrogant is an age old problem.
The thing you’re describing about bunching patients into general states with generic treatment - that’s the majority of GPs I’ve seen over the years, sadly. I don’t think it’s because of incompetence as much as economics. They have to see a certain number of patients and make things work.
They aren't going to take a stab at an uncommon diagnosis even if it occurs to them, if they might get sued if they're wrong.
Edit: I'm not trying to say Doctors deliberately diagnose wrong. Just that if there are two possible diagnoses, one common that matches some of the symptoms and one rare that matches all symptoms, doctors are still much more likely to diagnose the common one. Hoofbeats, horses, zebras, etc
Should they not report on peer reviewed articles published in Science? or only report published articles that fit your priors?
I take them as those code generation command line tools like create react app and such.
Stochastic parrots can code yes, but that does not make them experts. Don't trust them with your life.
[1] https://mediconsulta.net (DeepSeek)