I'm an interventional radiologist with a master's in computer science. People outside radiology don't get why AI hasn't taken over.
Can AI read diagnostic images better than a radiologist? Almost certainly the answer is (or will be) yes.
Will radiologists be replaced? Almost certainly the answer is no.
Why not? Medical risk. Unless the law changes, a radiologist will have to sign off on each imaging report. So say you have an AI that reads images primarily and writes pristine reports. The bottleneck will still be the time it takes for the radiologist to look at the images and validate the automated report. Today, radiologist read very quickly, with a private practice rads averaging maybe 60-100 studies per day (XRs, ultrasounds, MRIs, CTs, nuclear medicine studies, mammograms, etc). This is near the limit of what a human being can reasonably do. Yes, there will be slight gains at not having to dictate anything, but still having to validate everything takes nearly as much time.
Now, I'm sure there's a cavalier radiologist out htere who would just click "sign, sign, sign..." but you know there's a malpractice attorney just waiting for that lawsuit.
But this indicates lack of incentives to reduce healthcare costs by optimisation. If AI can do something well enough , and AI + humans surpass humans leading to costs reductions/ increased throughput this should be reflected in the workflows.
I feel that human processes have inertia and for lack of a better word, gatekeepers feel that new, novel approaches should be adopted slowly and which is why we are not seeing the impact, yet. Once a country with the right incentive structure (e.g. China ) can show that it can outperform and help improve the overall experience I am sure things will change.
While 10 years progress is a lot in ML, AI , in more traditional fields it probably is a blip to change this institutional inertia which will change generation by generation. All that is needed is an external actor to take the risk and show a step change improvement. Having experienced how healthcare in US I feel people are only scared to take on bold challenges
Most of your job security comes from arbitrary supply constraint and regulation. Which is another way of saying, it’s one regulation away from disappearing
Doesn't most of the stuff a radiologist does get double checked anyways by the doctor that orders the scan in the first place? I guess not a more typical screening scan like a mammogram. However, for anything else like a CT, MRI, Xray, etc. I expect the doctor/NP that ordered it in the first place will want to take a look at the image itself and not just the report on the image.
A primary physician (or NP) isn't in a position to validate the judgement of a specialist. Even if they had the training and skill (doubtful), responsibility goes up, not down. It's all a question of who is liable when things go wrong.
When Tesla demoed (via video) self-driving in 2016 with a claim "The person in the driver’s seat is only there for legal reasons. He is not doing anything. The car is driving itself" and then when they unveiled Semi in 2017 - I tweeted out and honestly thought that trucking industry is changed forever and it doesn't make sense to be starting in trucking industry. It's almost end of 2025 and either nothing out of it or just a small part of it panned out.
I think we all have become hyper-optimistic on technology. We want this tech to work and we want it to change the world in some fundamental way, but either things are moving very slowly or not at all.
Look at Waymo, not Robotaxi. Waymo is essentially the self driving vision I had as a kid, and ridership is growing exponentially as they expand. It's also very safe if you believe their statistics[0]. I think there's a saying about overestimating stuff in the short term and underestimating stuff in the long term that seems to apply here, though the radiologist narrative was definitely wrong.
Even though the gulf between Waymo and the next runner up is huge, it too isn't quite ready for primetime IMO. Waymos still suffer from erratic behavior at pickup/dropoff, around pedestrians, badly marked roads and generally jam on the brakes at the first sign of any ambiguity. As much as I appreciate the safety-first approach (table stakes really, they'd get their license pulled if they ever caused a fatality) I am frequently frustrated as both a cyclist and driver whenever I have to share a lane with a Waymo. The equivalent of a Waymo radiologist would be a model that has a high false-positive and infinitesimal false-negative rate which would act as a first line of screening and reduce the burden on humans.
I've seen a lot of young people (teens especially) cross active streets or cross in front of Waymos on scooters knowing that they'll stop. I try not to do anything too egregious, but I myself have begun using Waymo's conservative behavior as a good way to merge into ultra high density traffic when I'm in a car, or to cross busy streets when they only have a "yield to pedestrian" crosswalk rather than a full crosswalk. The way you blip a Waymo to pay attention and yield is beginning to move into the intersection, lol.
I always wonder if honking at a Waymo does anything. A Waymo stopped for a (very slow) pickup on a very busy one lane street near me, and it could have pulled out of traffic if it had gone about 100 feet further. The 50-ish year old lady behind it laid on her horn for about 30 seconds. Surreal experience, and I'm still not sure if her honking made a difference.
I honestly don't think we will have a clear answer to this question anytime soon. People will be in their camps and thats that.
Just to clarify, have you ridden in a Waymo? It didn't seem entirely clear if you just experienced living with Waymo or have ridden in it.
I tried it a few times in LA. What an amazing magical experience. I do agree with most of your assertions. It is just a super careful driver but it does not have the full common sense that a driver in a hectic city like LA has. Sometimes you gotta be more 'human' and that means having the intuition to discard the rules in the heat of the moment (ex. being conscious of how cyclists think instead of just blindly following the rules carefully, this is cultural and computers dont do 'culture').
> Waymos still suffer from erratic behavior at pickup/dropoff, around pedestrians, badly marked roads and generally jam on the brakes at the first sign of any ambiguity.
As do most of the ridesharing drivers I interact with nowadays, sadly.
The difference is that Waymo has a trajectory that is getting better while human rideshare drivers have a trajectory that is getting worse.
Society accepts that humans make mistakes and considers it unavoidable, but there exists a much higher bar expected of computers/automation/etc. even if a waymo is objectively safer in terms of incidents per miles driven, one fatality makes headlines and adds scrutiny about “was it avoidable?”, whereas humans we just shrug.
I think the theme of this extends to all areas where we are placing technology to make decisions, but also where no human is accountable for the decision.
Society only cares about the individual and no one else. If Uber/Lyft continue to enshittify with drivers driving garbage broken down cars, drivers with no standards (ie. having just smoked weed) and ever rising rates, eventually people will prefer the Waymos.
Honestly, once a traffic island city (like Singapore) or some other small nation state adopts self driving only within its limits and shows that it is much easier when all are self driving I think the opposition to the change will slowly reduce.
Rain, Snow etc. are still challenges but needs a bold bet in a place that wants to show how futuristic it is. The components are in place (Waymo cars), what is needed is high enough labor cost to justify the adoption.
I agree with both comments here. I wonder what the plausibility of fully autonomous trucking is in the next 10-30 years...
Is there any saying that exists about overestimating stuff in the near term and long term but underestimating stuff in the midterm? Ie flying car dreams in the 50s etc.
> ... but underestimating stuff in the midterm? Ie flying car dreams in the 50s etc.
We still don't have flying cars 70 years later, and they don't look any more imminent than they did then. I think the lesson there is more "not every dream eventually gets made a reality".
Gates seems more calm and collected having gone through the trauma of almost losing his empire.
Musk is a loose cannon having never suffered the consequences of his actions (ie. early Gates and Jobs) and so he sometimes gets things right but will eventually crash and burn having not had the fortune of failing and maturing early on in his career(he is now past the midpoint of his career with not enough buffer to recover).
Waymo still have the ability to remotely deal with locations the AI has problems; I'd love to know what type of percentage of trips need to do that now.
Having that escape together with only doing tested areas makes their job a LOT easier.
(Not that it's bad - it's a great thing and I wish for it here!)
Waymo is very impressive, but also demonstrates limitations of these systems. Waymo vehicles are still getting caught performing unsafe driving maneuvers, they get stuck alleys in numbers, and responders have trouble getting them to acknowledge restricted areas. I am very supportive of this technology, but also highly skeptical as long as these vehicles are directly causing problems for me personally. Driving is more than a technical challenge, it involves social communication skills that automated vehicles do not yet have.
It's limited to a few specific markets though. My bet is they aren't going to be able to roll it out widely easily. Probably need to do years of tests in each location to figure out the nuances of the places.
Yeah, I have no idea if Waymo will ever be a rural thing honestly, mostly for economic reasons. I'm skeptical it would get serious suburban usage this decade too. But for major cities where less than 80% of people own cars, test time doesn't seem to be making a difference. They've been expanding in Austin and Atlanta, seemingly with less prep time than Phoenix and San Fran.
If I were in charge of Waymo, I’d roll out in snowy places last. The odds of a “couldn’t be avoided” accident is much higher in snow/ice. I’d want an abundance of safety data in other places to show that the cars are still safe, and it was the snow instead of the tech that caused the accident.
Atlanta seems to be a bit contradictory to some of your other thoughts.
The city itself is relatively small. A vast majority of area population lives distributed across the MSA, and it can create hellish traffic. I remember growing up thinking 1+ hour commutes were just a fact of life for everyone commuting from the suburbs.
Not sure what car ownership looks like, and I haven’t been in years, but I’d imagine it’s still much more than just 20%
Austin is also a car city, everyone has a car there. Public transit in Austin is a joke, and Waymo can't get on the highway so it's only useful for getting back to your hotel from Rainey Street, and maybe back to your dorm from the Drag, but nobody is using Waymo to commute from Round Rock
I saw this timeline a while ago: https://www.reddit.com/r/waymo/s/mSm0E3yYTY that shows their timeline in each city. Shows Atlanta at just over a year. I think once they've handled similar cities it gets easier and easier to add new ones.
It is the usual complexity rule of software: solving 80% of the problem is usually pretty easy at only takes about 50% of the estimated effort, it is the remaining 20% that takes up the remaining 90% of estimated effort (thus the usual schedule overruns).
The interesting thing is that there are problems for which this rule applies recursively. Of the remaining 20%, most of it is easier than the remaining 20% of what is left.
Most software ships without dealing with that remaining 20%, and largely that is OK; it is not OK for safety critical systems though.
for me i have been riding in waymos the last year and have been very pleased with the results. i think we WANT this technology to move faster but the some of the challenges at the edges take a lot of time and resources to solve, but not fundamentally unsolvable.
Much like phone-a-friend, when the Waymo vehicle encounters a particular situation on the road, the autonomous driver can reach out to a human fleet response agent for additional information to contextualize its environment. The Waymo Driver does not rely solely on the inputs it receives from the fleet response agent and it is in control of the vehicle at all times. As the Waymo Driver waits for input from fleet response, and even after receiving it, the Waymo Driver continues using available information to inform its decisions. This is important because, given the dynamic conditions on the road, the environment around the car can change, which either remedies the situation or influences how the Waymo Driver should proceed. In fact, the vast majority of such situations are resolved, without assistance, by the Waymo Driver.
After learning that the Amazon Go store was power by hundreds of people watching video because the AI could not handle it was a real eye opener for me.
Is this why Waymo is slow to expand, not enough remote drivers?
Maybe that is where we need to be focused, better remote driving?
> Maybe that is where we need to be focused, better remote driving?
I think maybe we can and should focus on both. Better remote driving can be extended into other equipment operations as well - remote control of excavators and other construction equipment. Imagine road construction, or building projects, being able to be done remotely while we wait for better automation to develop.
Most work is actually in oversight and getting the train to run when parts fail. When running millions of machines 24/7 there is always a failing part. Also understanding gesticulation humans and running wildlife is not yet (fully) automatable.
I realized long ago that full unattended self driving requires AGI. I think Elon finally figured that out. So now LLMs are going to evolve into AGI any moment. Um no. Tesla (and others) have effectively been working on AGI for 10 years with no luck
You should either elaborate on your argument, or at least provide further reading that clarifies your point of contention. This kind of low effort nerd-sniping contributes nothing.
It's commonly brought up saying, and I don't think it's too far from the truth.
Driving under every condition requires a very deep level of understanding of the word. Sure, you can get to like 60% by a simple robot vacuum logic, and to like 90% with what e.g. Waymo does. But the remaining 10% is crazy complex.
What about a plastic bag floating around on a highway? The car can see it, but is it an obstacle to avoid? Should it slam the brakes? And there are a bunch of other extreme examples (what about a hilly road on a Greek island where people just honk to notify the other side that they are coming, without seeing them?)
Meanwhile, it’s my feeling that technology is moving insanely fast but people are just impatient. You move the bar and the expectations move with it. I think part of the problem is that the market rewards execs who set expectations beyond reality. If the market was better at rewarding outcomes not promises, you’d see more reasonable product pitches.
How have expectations moved on self driving cars? Yes, we're finally getting there, but adoption is still tiny relative to the population and the cars that work best (Waymo) are still humongously expensive + not available for consumer purchase.
"I think we all have become hyper-optimistic on technology. We want this tech to work and we want it to change the world in some fundamental way, but either things are moving very slowly or not at all."
It's almost end of 2025 and either nothing out of it or just a small part of it panned out.
The truck part seems closer than the car part.
There are several driverless semis running between Dallas, Houston, and San Antonio every day. Fully driverless. No human in the cab at all.
Though, trucking is an easier to solve problem since the routes are known, the roads are wide, and in the event of a closure, someone can navigate the detour remotely.
This story (the demand for Radiologists) really shows a very important thing about AI: It's great when it has training data, and bad at weird edge cases.
Gee, seems like about the worst fucking thing in the world for diagnostics if you ask me, but what do I know, my degree is in sandwiches and pudding.
It's not about optimism. It is well established in the industry that Tesla's hardware-stack gives them 98% accuracy at the very most. But those voices are drowned by the marketing bravado.
In the case of Musk it has worked out. His lies have earned him a fortune and now he asks Tesla to pay him out with a casual 1 trillion paycheck.
This is such a stereotypical SF / US based perspective.
Easy to forget the rest of the world does not and never has ticked this way.
Don't get me wrong, optimism and thinking of the future are great qualities we direly need in this world on the one hand.
On the other, you can't outsmart physics.
We've conquered the purely digital realm in the past 20 years.
We're already in the early years of the next phase were the digital will become ever more multi-modal and make more inroads into the physical world.
So many people bring an old mindset to a new context, where maring of errors, cost of mistakes or optimizing the last 20% of a process is just so vastly different than a bit of HTML, JS and backend infra.
The universe has a way with being disappointing. This isn't to say that life is terrible and we should have no optimism. Rather, that things generally work out for the better, but usually not in the way we'd prefer them to.
Waymo has worked out. I’ve taken one so many times now I don’t even think about it. If Waymo can pull this off in NYC I believe it will absolutely be capable of long distance trucking not that far in the future.
Trucks are orders of magnitude more dangerous. I wouldn’t be surprised if Waymo is decades away from being able to operate a long haul truck on the open interstate.
For trucking I think self driving can be, in the short term, an opportunity for owner-operators. An owner-operator of a conventional truck can only drive one truck at a time, but you could have multiple self driving trucks in a convoy led by a truck manned by the owner-operator. And there might be an even greater opportunity for this in Europe thanks to the low capacity of European freight rail compared to North America.
I used to think this sort of thing too. Then a few years ago I worked with a SWE who had experience in the trucking industry. His take was that most trucking companies are too small scale to benefit from this. The median trucking operation is basically run by the owner's wife in a notebook or spreadsheet- and so their ability to get the benefits of leader/follower mileage like that just doesn't exist. He thought that maybe the very largest operators- Walmart and Amazon- could benefit from this, but he thought that no one else could.
This was why he went into industrial robotics instead, where it was clear that the finances could work out today.
Yeah, I guess the addressable market of “truck owners who can afford to buy another truck but not hire another driver” might be smaller than I thought.
It's also like nobody learns from the previous hype cycles. Short term overly optimistic predications followed by disillusionment and then long term benefits which deliver on some of the early promises.
For some reason, enthusiasts always think this time is different.
The best story I heard about machine learning and radiology was when folks were racing to try to detect COVID in lung X-rays.
As I recall, one group had fairly good success, but eventually someone figured out that their data set had images from a low-COVID hospital and a high-COVID hospital, and the lettering on the images used different fonts. The ML model was detecting the font, not the COVID.
If you're not at a university, try searching for "AI for radiographic COVID-19 detection selects shortcuts over signal" and you'll probably be able to find an open-access copy.
I remember a claim that someone was trying to use an ML model to detect COVID by analyzing the sound of the patient coughing.
I couldn't for the life of me understand how this was supposed to work. If the coughing of COVID patients (as opposed to patients with other respiratory illnesses) actually sounds meaningfully different in a statistically meaningful way (and why did they suppose that it would? Phlegm is phlegm, surely), surely a human listener would have been able to figure it out easily.
Anecdotes like this are informative as far as they go, but they don't say anything at all about the technique itself. Like your story about the fonts used for labeling, essentially all of the drawbacks cited by the article come down to inadequate or inappropriate training methods and data. Fix that, which will not be hard from a purely-technical standpoint, and you will indeed be able to replace radiologists.
Sorry, but in the absence of general limiting principles that rule out such a scenario, that's how it's going to shake out. Visual models are too good at exactly this type of work.
The issue is that in medicine, much like automobiles, unexpected failure modes may be catastrophic to individual people. “Fixing” failure modes like the above comment is not difficult from a technical standpoint, that’s true, but you can only fix it once you’ve identified it, and at that point you may have a dead person/people. That’s why AI in medicine and self driving cars are so unlike AI for programming or writing and move comparatively at a snails pace.
Yet self-driving cars are already competitive with human drivers, safety-wise, given responsible engineering and deployment practices.
Like medicine, self-driving is more of a seemingly-unsolvable political problem than a seemingly-unsolvable technical one. It's not entirely clear how we'll get there from here, but it will be solved. Would you put money on humans still driving themselves around 25-50 years from now? I wouldn't.
These stories about AI failures are similar to calling for banning radiation therapy machines because of the Therac-25. We can point and laugh at things like the labeling screwup that pjdesno mentioned -- and we should! -- but such cases are not a sound basis for policymaking.
> Yet self-driving cars are already competitive with human drivers, safety-wise, given responsible engineering and deployment practices.
Are they? Self driving cars only operate in a much safer subset of conditions that humans do. They have remote operators who will take over if a situation arises outside of the normal operating parameters. That or they will just pull over and stop.
When it weren't for the font it might be anomalies in the image taking or even in the encoder software. You can never really be sure, what exactly the ML is detecting.
Exactly. A marginally higher image ISO at one location vs a lower ISO at another could potentially have a similar effect, and it would be quite difficult to detect.
The point here is that the radiologists has a concept of knowing which light patterns are sensible to draw conclusions from and which not, because the radiologist has a concept of real world 3D objects.
> Three things explain this. First,... Second, attempts to give models more tasks have run into legal hurdles: regulators and medical insurers so far are reluctant to approve or cover fully autonomous radiology models. Third, even when they do diagnose accurately, models replace only a small share of a radiologist’s job. Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
Everything else besides the above in TFA is extraneous. Machine learning models could have absolute perfect performance at zero cost, and the above would make it so that radiologists are not going to be "replaced" by ML models anytime soon.
I only came to this thread to say that this is completely untrue:
>Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
The vast majority of radiologists do nothing other than: come in (or increasingly, stay at home), sit down at a computer, consume a series of medical images while dictating their findings, and then go home.
If there existed some oracle AI that can always accurately diagnose findings from medical images, this job literally doesn't need to exist. It's the equivalent of a person staring at CCTV footage to keep count of how many people are in a room.
Agreed, I'm not sure where the OP from TFA is working but around here, radiologists have all been bought out and rolled into Radiology As A Service organizations. They work from home or at an office, never at a clinic, and have zero interactions with the patient. They perform diagnosis on whatever modality is presented and electronically file their work into their EMR. I work with a couple such orgs on remote access and am familiar with others, it might just be a selection bias on my side but TFA does not reflect my first-hand experience in this area.
Interesting - living near a large city, all of the radiologists I know work for hospitals, spending more of their day in the hospital reading room versus home, including performing procedures, even as diagnostic radiologists.
Radiologists are often the ones who are the "brains" of medical diagnosis. The primary care or ER physician gets the patient scanned, and the radiologist scrolls through hundreds if not thousands of images, building a mental model of the insides of the patient's body and then based on the tens of thousands of cases they've reviewed in the past, as well as deep and intimate human anatomical knowledge, attempts to synthesize a medical diagnosis. A human's life and wellness can hinge on an accurate diagnosis from a radiologist.
>consume a series of medical images while dictating their findings, and then go home.
In the same fashion as construction worker just shows up, "performs a series of construction tasks", then go home. We just need to make a machine that performs "construction tasks" and we can build cities, railways and road networks for nothing but the cost of the materials!
Perhaps this minor degree of oversimplification is why the demise of radiologists have been so frequently predicted?
If they had absolute perfect performance at zero cost, you would not need a radiologist.
The current "workflow" is primary care physician (or specialist) -> radiology tech that actually does the measurement thing -> radiologist for interpretation/diagnosis -> primary care physician (or specialist) for treatment.
If you have perfect diagnosis, it could be primary care physician (or specialist) -> radiology tech -> ML model for interpretation -> primary care physician (or specialist.
If we're talking utopian visions, we can do better than dreaming of transforming unstructured data into actionable business insights. Let's talk about what is meaningfully possible: Who assumes legal liability? The ML vendor?
PCPs don't have the training and aren't paid enough for that exposure.
To understand why, you would really need to take a good read of the average PCP's malpractice policy.
The policy for a specialist would be even more strict.
You would need to change insurance policies before your workflow was even possible from a liability perspective.
Basically, the insurer wants, "a throat to choke", so to speak. Handing up a model to them isn't going to cut it anymore than handing up Hitachi's awesome new whiz-bang proton therapy machine would. They want their pound of flesh.
In that scenario, the "throat to choke" would be the primary care physician. We won't think of it as an "ML radiologist", just as getting some kind of physical test done and bringing it to the doctor for interpretation.
If you're getting a blood test, the pipeline might be primary care physician -> lab with a nurse to draw blood and machines to measure blood stuff -> primary care physician to interpret the test results. There is no blood-test-ologist (hematologist?) step, unlike radiology.
Anyway, "there's going to be radiologists around for insurance reasons only but they don't bring anything else to patient care" is a very different proposition from "there's going to be radiologists around for insurance reasons _and_ because the job is mostly talking to patients and fellow clinicians".
Let’s suppose I go to the doctor and get tested for HIV. There isn’t a specialist staring at my blood through a microscope looking for HIV viruses, they put my blood in a machine and the machine tells them, positive or negative. There is a false positive rate and a false negative rate for the test. There’s no fundamental reason you couldn’t put a CT scan into a machine the same way.
Pretty much everything has false positives and false negatives. Everything can be reduced to this.
Human radiologists have them. They can miss things: false negative. They can misdiagnose things: false positive.
Interviews have them. A person can do well, be hired and turn out to be bad employee: false positive. A person who would have been a good employee can do badly due to situational factors and not get hired: false negative.
The justice system has them. An innocent person can be judged guilty: false positive. A guilty person can be judged innocent: false negative.
All policy decisions are about balancing out the false negatives against the false positives.
Medical practice is generally obsessed with stamping out false negatives: sucks to be you if you're the doctor who straight up missed something. False positives are avoided as much as possible by defensive wording that avoids outright affirming things. You never say the patient has the disease, you merely suggest that this finding could mean that the patient has the disease.
Hiring is expensive and firing even more so depending on jurisdiction, so corporations want to minimize false positives as much as humanly possible. If they ever hire anyone, they want to be sure it's absolutely the right person for them. They don't really care that they might miss out on good people.
There are all sorts of political groups trying to tip the balance of justice in favor of false negatives or false positivies. Some would rather see guilty go free than watch a single innocent be punished by mistake. Others don't care about innocents at all. I could cite some but it'd no doubt lead to controversy.
They didn’t say there wouldn’t need to be change related to insurance. They obviously mean that, change included, a perfect model would move to their described workflow (or something similar).
HackerNews is often too quick to reply with a “well actually” that they miss the overall point.
>Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
How often do they talk to patients? Every time I have ever had an x-ray, I have never talked to a radiologist. Fellow clinicians? Train the xray tech up a bit more.
If the mote is 'talking to people' that is a mote that doesn't need an MD, or at least not a full specialization MD. ML could kill radiologist MD, radiologist could become the job title of a nurse or x-ray tech specialized in talking to people about the output.
That's fine. But then the xray tech becomes the radiologist, and that becomes the point in the workflow that the insurer digs out the malpractice premiums.
In essence, your xray techs would become remarkably expensive. Someone is talking to the clinicians about the results. That person, whatever you call them, is going to be paying the premiums.
As a patient I don't think I've ever even talked to any radiologist that actually analyzed my imaging. Most of the times my family or I have had imaging done the imaging is handled by a tech who just knows how to operate the machines while the actual diagnostic work gets farmed out to remote radiologists who type up an analysis. I don't even think the other doctors I actually see ever directly talk to those radiologists.
It really depends on the specifics of the clinical situation; for a lot of outpatient radiology scenarios the patient and radiologist don't directly interact, but things can be different in an inpatient setting and then of course there are surgical and interventional radiology scenarios.
Look, if we were okay with tolerating less regulation in medicine, and dismantled AMA, Hinton would have proven to be right by now and everyone would have been happier
People love to bring this up, and it was a silly thing to say -- particularly since he didn't seem to understand that radiologists only spend a small part of their time reading scans.
But he said it in the context of a Q&A session that happened to be recorded. Unless you're a skilled politician who can give answers without actually saying anything, you're going to say silly things once in a while in unscripted settings.
Besides that, I'd hardly call Geoffrey Hinton an AI evangelist. He's more on the AI doomer side of the fence.
No, this was not an off-hand remark. He made a whole story comparing the profession to the coyote from road runner “they’ve already run of the cliff but don’t even realize it”. It was callous, and showed a total ignorance of the fact that medicine might be more than pixel classification.
Radiologists, here, mostly sit at home, read scan and dictate reports. They rarely talk to other doctors and talking to a patient is beyond them. They are some of the specialists with the best salary.
With interventional radiologists and radio-oncologists it's different but were talking about radiologists here...
You practice in Québec ? If so I am quite surprised, because my wife had a lot of scans and we never met a radiologists who wasn't a radio-oncologist. And her oncologist never talked with the radiologists either. The communication between them was always through written demands and reports. And the situation is similar between her neurologist and the radiologists.
By the way, even if I sound dismissive I have great respect for the skills required by your profession. Reading an IRM is really hard when you have the radiologist report in hand and to my untrained eyes it's impossible without it!
And since you talk to patients frequently, I have an even greater respect of you as a radiologist.
Then it's an organizational problem (or choice) in the specific hospital where my wife is treated/followed and I apologize to all radiologists that actually talk to peoples in a professional capacity!
Or maybe it's related to socialized Healthcare because in the article there is a breakdown of the time spent by a radiologists in Vancouver and talking to patients isn't part of it.
I would argue an "AI doomer" is a negatively charged type of evangelist. What the doomer and the positive evangelist have in common is a massive overestimation of (current-gen) AI's capabilities.
It's the power of confidence and credentials in action. Which is why you should, when possible, look at the underlying logic and not just the conclusion derived from it. As this catches a lot of fluff that would otherwise be Trojan-Horsed into your worldview.
I think in general this lack affects almost all areas of human endeavor. All my speech teaching my kids how to think clearly, to young software engineers about how to build software in a some giant ass bureaucracy, how to debug some tricky problem, none of that sort of discovering truth one step at a time or teaching new stuff is in blogs or anything outside the moment.
When I do write something up, it is usually very finalized at that time; the process of getting to that point is not recorded.
The models maybe need more naturalistic data and more data from working things out.
Let's assume the last person that entered their radiologist training started then and the training lasts 5 years. At the end of their training the year is 2021 and they are around 31. So that means they will practice medicine for cca 30 years which would put the calendar at around 2051. I'd wager in 25 years we'd get there so I think his opinion still has a large percentage of being correct.
People can't tell what they'll eat next sunday but they'll predict AGI and singualrity in 25 years. It's comfy because 25 years seems like a lot of time, it isn't.
Let’s say we do manage to develop a model that can replace radiologists in 20 years. But we stop training them today. What happens 15 years from now when we don’t have nearly enough radiologists.
Why do we assume that radiologists would have literally 0% involvement in the radiology workflow?
I could see the assumption that one radiologist supervises a group of automated radiology machines (like a worker in an automated factory). Maybe assume that they'd be delegated to an auditing role. But that they'd go completely extinct? There's no evidence of, even historically, a service being consumed that has zero human intervention.
> the training lasts 5 years. At the end of their training the year is 2021
The training lasts 5 years, 2021 - 5 = 2016 If they stopped accepting people into the radiologist program but let people already in to finish, then you would stop having new radiologist in 2021.
Training is a lot longer than that in Québec, radiology is a specialty, so they must first do their 5 years in medicine, followed by a 5 year diagnostic radiology residency program. And it's frequently followed by a 2 years fellowship.
In May earlier this year, the New York Times had a similar article about AI not replacing radiologists:
https://archive.is/cw1Zt
It has similar insights, and good comments from doctors and from Hinton:
“It can augment, assist and quantify, but I am not in a place where I give up interpretive conclusions to the technology.”
“Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.”
Dr. Hinton agrees. In retrospect, he believes he spoke too broadly in 2016, he said in an email. He didn’t make clear that he was speaking purely about image analysis, and was wrong on timing but not the direction, he added.
The obvious answer is regulation and legal risk. It's the same reason retail pharmacists still have jobs despite their currently being slow poor-quality vending machines.
As a doctor and full stack engineer, I would never go into radiology or seek further training in it. (obviously)
AI is going to augment radiologists first, and eventually, it will start to replace them. And existing radiologists will transition into stuff like interventional radiology or whatever new areas will come into the picture in the future.
>AI is going to augment radiologists first, and eventually, it will start to replace them.
I am a medical school drop-out — in my limited capacity, I concur, Doctor.
My dentist's AI has already designed a new mouth for me, implants &all ("I'm only doing 1% of the finish-work: whatever the patient says doesn't feel just quite right, yet"—myDMD). He then CNCs in-house on his $xxx,xxx 4-axis.
IMHO: Many classes of physicians are going to be reduced to nothing more than malpractice-insurance-paying business owners, MD/DO. The liability-holders, good doctor.
In alignment with last week's (H)(1)(b) discussion, it's interesting to note that ~30% of US physician resident "slots" (<$60kUSD salary) are filled by these foreigner visa-holders (so: +$100k cost per applicant, amortized over a few years of training, each).
As a radiologist and full stack engineer, I’m not particularly worried about the profession going away. Changing, yes, but not more so than other medical or non-medical careers.
What’s your take on pharmacists? To my naive eyes, that seems like a certainty for replacement. What extra value does human judgement bring to their work?
My wife is a clinical pharmacist at a hospital. I am a SWE working on AI/ML related stuff. We've talked about this a lot. She thinks that the current generation of software is not a replacement for what she does now, and finds the alerts they provide mostly annoying. The last time this came up, she gave me two examples:
A) The night before, a woman in her 40's came in to the ER suffering a major psychological breakdown of some kind (she was vague to protect patient privacy). The Dr prescribed a major sedative, and the software alerted that they didn't have a negative pregnancy test because this drug is not approved for pregnant women and so should not be given. However, in my wife's clinical judgement- honed by years of training, reading papers, going to conferences, actual work experience and just talking to colleagues- the risk to a (potential) fetus from the drug was less than the risk to a (potential) fetus from mom going through an untreated mental health episode and so she approved the drug and overrode the alert.
B) A prescriber had earlier in that week written a script for Tylenol to be administered "PR" (per-rectum) rather than PRN (per requisite need). PR Tylenol is a perfectly valid thing that is sometimes the correct choice, and was stocked by the hospital for that reason. But my wife recognized that this wasn't one of the cases where that was necessary, and called the nurse to call the prescriber to get that changed so the nurse wouldn't have to give them a Tylenol suppository. This time there were no alerts, no flags from the software, it was just her looking at it and saying "in my clinical judgement, this isn't the right administration for this situation, and will make things worse".
So someone- with expensively trained (and probably licensed) judgement- will still need to look over the results of this AI pharmacist and have the power to override its decisions. And that means that they will need to have enough time per case to build a mental model of the situation in their brain, figure out what is happening, and override if necessary. And it needs to be someone different from the person filling out the Rx, for Swiss cheese model of safety reasons.
Congratulations, we've just described a pharmacist.
> And it needs to be someone different from the person filling out the Rx, for Swiss cheese model of safety reasons.
This is something I question. If you go to a specialist, and the specialist judges that you need surgery, he can just schedule and perform the surgery himself. There’s no other medical professional whose sole job is to second-guess his clinical judgment. If you want that, you can always get a second opinion. I have a hard time buying the argument that prescription drugs always need that second level of gatekeeping when surgery doesn’t.
So, the main reason for the historical separation (in the European tradition) between doctor and pharmacist was profit motive- you didn't want the person prescribing to have a financial stake in their treatment, else they will prescribe very expensive medicine for all cases. And surgeons in particular do have a profit motive- they are paid per service- and it is well known within the broader medical community that surgeons will almost always choose to cut. And we largely gate-keep this with the primary care physician providing a recommendation to the specialist. The PCP says "this may be something worth treating with surgery" when they recommend you go see a specialist rather than prescribing something themselves, and then the surgeon confirms (almost always).
That pharmacists also provide a safety check is a more modern benefit, due to their extensive training and ability to see all of the drugs that you are on (while a specialist only knows what they have prescribed). And surgeons also have a team to double-check them while they are operating, to confirm that they are doing the surgery on the correct side of the body, etc. Because these safety checks are incredibly important, and we don't want to lose them.
I am a pharmacist who dabbles in web dev. We should easily be replaced because all of our work on checking pill images and drug interactions are actually already automated, or the software already tells us everything.
If every doctor agreed to electronically prescribe (instead of calling it in, or writing it down) using one single standard / platform / vendor, and all pharmacy software also used the same platform / standard, then our jobs are definitely redundant.
I worked at a hospital where basically doctors and pharmacists and nurses all use the same software and most of the time we click approve approve approve without data entry.
Of course we also make IVs and compounds by hand, but that's a small part of our job.
I'm not a doc or a pharmacist (though I am in med school) and I'm sure there are areas that AI could do some of a pharmacists job but on the outpatient side they do things like answering questions for patients and helping them interpret instructions that I don't think we want AI to do... or at least I really doubt an AIs ability to gauge how well someone is understanding instructions and augment how it explains something based on that assessment... on the inpatient side, I have seen pharmacists help physicians grapple with the pros and cons of certain treatments and make judgement calls about dosing that I think it would be hard to trust an AI to do because there is no "right" answer really. It's about balancing trade offs.
IDK, these are just limitations - people that really believe in AI will tell you there is basically nothing it can't do... eventually. I guess it's just a matter of how long you want to wait for eventually to come.
I work on a kiosk (MedifriendRx) which, to some degree "replaces" pharmacists and pharmacy staff.
The kiosk is placed inside of a clinic/hospital setting, and rather than driving to the pharmacy, you pick up your medications at the kiosk.
Pharmacists are currently still very involved in the process, but it's not necessarily for any technical reason. For example, new prescriptions are (by most states' boards of pharmacies) required to have a consultation between a pharmacist and a patient. So the kiosk has to facilitate a video call with a pharmacist using our portal. Mind you, this means the pharmacist could work from home, or could queue up tons of consultations back to back in a way that would allow one pharmacist to do the work of 5-10 working at a pharmacy, but they're still required in the mix.
Another thing we need to do for regulatory purposes is when we're indexing the medication in the kiosk, the kiosk has to capture images of the bottles as they're stocked. After the kiosk applies a patient label, we then have to take another round of images. Once this happens, this will populate in the pharmacist portal, and a pharmacist is required to take a look at both sets of images and approve or reject the container. Again, they're able to do this all very quickly and remotely, but they're still required by law to do this.
TL;DR I make an automated dispensing kiosk that could "replace" pharmacists, but for the time being, they're legally required to be involved at multiple steps in the process. To what degree this is a transitory period while technology establishes a reputation for itself as reliable, and to what degree this is simply a persistent fixture of "cover your ass" that will continue indefinitely, I cannot say.
Pharmacists are not going to be replaced, their jobs like most other jobs touched by AI will evolve, possibly shrink in demand but won't completely dissapear. AI is a tool that some professional has to use after all.
There's a number of you (engineer + doctor), though quite rare. I have a few friends who are engineers as well as doctors. You're like unicorns in your field. The Neo and Morpheus of the medical industry - you can see things and understand things that most people cant in your typical field (medicine). Kudos to you!
This was actually my dream career path when I was younger. Unfortunately there's just no way I would have afforded the time and resources to pursue both, and I'd never heard of Biomedical Engineering where I grew up.
As a doctor and full stack engineer you’d have a perfect future ahead of you in radiology - the profession will not go away, but will need doctors who can bridge the full medical-tech range
I wouldn't trust a non-radiologist to safely interpret the results of an AI model for radiology, no matter how well that model performs in benchmarks.
Similar to how a model that can do "PhD-level research" is of little use to me if I don't have my own PhD in the topic area it's researching for me, because how am I supposed to analyze a 20 page research report and figure out if it's credible or not?
The notion of “PhD-level research” is too vague to be useful anyways. Is it equivalent to a preprint, a poster, a workshop paper, a conference paper, a journal submission, or a book? Is it expected to pass peer review in a prestigious venue, a mid-tier venue, or simply any venue at all?
There’s wildly varying levels of quality among these options, even though they could all reasonably be called “PhD-level research.”
I'm a professor who trains PhDs in cryptography, and I can say that it genuinely does have knowledge equivalent to a PhD student. Unfortunately I've never gotten it to produce a novel result. And occasionally it does frightening stuff, like swapping the + and * in a polynomial evaluation when I ask it to format a LaTeX algorithm.
What we need is a mandate for AI transformation of Radiology: Radiologists must be required to use AI every day on X% of scans, their productivity must double with the use of AI or they'll get fired, etc. To quote CEOs everywhere: AI is a transformative technology unlike any we've ever seen in our careers, and we must embrace it in a desperate FOMO way, anything else is unacceptable.
This article is pretty good. My current work is transitioning CV models in a large, local hospital system to a more unified deployment system, and much of the content aligns with conversations we have with providers, operations, etc..
I think the part that says models will reduce time to complete tasks and allow providers to focus on other tasks is on point in particular. For one CV task, we’re only saving on average <30min of work per study, so it isn’t massive savings from a provider’s perspective. But scaled across the whole hospital, it’s huge savings
Oh yes it is. I have worked on projects where highly trained specialized doctors have helped train the models (or trained them themselves) to catch random very difficult to notice conditions via radiology. Some of these systems are deployed at different hospitals and medical facilities around the country. The radiologist still does there job, but some odd, random hard to notice conditions, AI is a literal life saver. For example, pancreas divisum, a abnormality in the way the pancreas ducts fail to fuse/etc can cause all kinds of insane issues. But its not something most people know about or look for. AI can pick that up in a second. It can then alert the radiologist of an abnormality and they can then verify. It's enhacing the capabilties of radiologists.
It's interesting to see people fighting so hard to preserve these jobs. Do people want to work that badly? If a magic wand can do everything radiologists can do, would we embrace it or invent reasons to occupy 40+ hours a week of time anyway? If a magic wand, might be on the horizon, shouldn't we all be fighting to find it and even finding ways to tweak our behaviors to maximize the amount of free time that could be generated?
People like the promised stability that comes with certain jobs. Some jobs are sold as "study this, get the GPA, apply to this university and do these things and you will get a stable job at the end". AI plans to disrupt this path.
People enjoy the comfort of consistent food and housing. People also enjoy serving their community. Working helps provide that. So for folks to be willing to sacrifice their security and comfort to get to the horizon of the new day with greater leisure time, it can be scary for many. Especially when you have to make a leap of belief that AI is a magic wand changing your world for the better. Is that supported by the evidence? It's quite the leap in belief and life change. Hesitancy seems appropriate to me.
It is precisely the attraction of the vision that makes people fight so hard to preserve these jobs.
Because we know how well the jobs address a need, and we also know how many times throughout history we have been promised magic wands that never quite showed up.
And guess who is best equipped to measure the actual level of “magic”? Experts like radiologists. We need them the most along the way, not the least.
If a magic wand actually shows up, it will be obvious to everyone and we’ll all adopt it voluntarily. Just like thousands of innovations in history.
It's not that so much as no one wants to lose their jobs due to innovation. Just look at typewriter repairmen, TV, radio, even Taxi drivers at one point etc. One day AI and automation will make many jobs redundant, so rather than resisting the march forward of technology, prepare for it and find a way to work alongside, not against innovation.
One thing people aren't talking about is liability.
At the end of the day if the radiologist makes an error the radiologist gets sued.
If AI replaces the radiologist then it is OpenAI or some other AI company that will get sued each and every time the AI model makes a mistake. No AI company wants to be on the hook for that.
So what will happen? Simple. AI will always remain just a tool to assist doctors. But there will always be a disclaimer attached to the output saying that ultimately the radiologist should use his or her judgement. And then the liability would remain with the human not the AI company.
Maybe AI will "replace" radiologists in very poor countries where people may not have had access to radiologists in the first place. In some places in the world it is cheap to get an xray but still can be expensive to pay someone to interpret it. But in the United States the fear of malpractice will mean radiologists never go away.
EDIT: I know the article mentions liability but it mentions it as just one reason among many. My contention is that liability will be the fundamental reason radiologists are never replaced regardless of how good the AI systems get. This applies to other specialities too.
>At the end of the day if the radiologist makes an error the radiologist gets sued.
Are you sure? Who would want to be a radiologist then when a single false negative could bankrupt you? I think it's more likely that as long as they make a best effort at trying to classify correctly then they would be fine.
Yeah, I mean it's analogous to car insurance for self driving cars. People, including lawyers, insurers and courts are just averse to it intuitively. I'm not saying they are wrong or right, but it's how it is.
I believe medical AI will probably take hold first in a poorer countries where the existing care is too bad/unaffordable, then as it proves itself there, it may slowly find its way to richer countries.
But probably lobbying will be strong against it, just as you can't get cheap generic medications made in India if you live in the US.
While a lot of this rings true, I think the analysis is skewed towards academic radiology. In private practice, everything is optimized for throughput, so the idea that most rads spend less than half of their time reading studies i think is probably way off.
So instead of having to train and employ radiologists, we will train and employ radiologists and pay for the AI inference. Excuse me, but how is this beneficial in any way? It's trivially more expensive, and the result has the same quality? And productivity is also the same?
> Some products can reorder radiologist worklists to prioritize critical cases, suggest next steps for care teams, or generate structured draft reports that fit into hospital record systems.
A lot of the tech in the space is workflow related right now. The AI scans triage to the top those cases that are deemed clinically significant, potentially saving time. I can’t tell you it isn’t solely responding to the referring doctor’s urgent stamp though.
Not sure about other hospital systems, but the one I work at is developing CV systems to help fill workforce gaps in places where there isn’t as many trained professionals or even resources to train professionals
>"they struggle to replicate this performance in hospital conditions"
Are there systematic reasons why radiologists in hospitals are inaccurately assessing the AI's output? If the AI models are better than humans in testing novel data then, well, the thing that has changed in a hospital situation compared to the AI-Human testing environment is not the AI, it is the human, under less controlled constraints, additional pressures, workloads, etc. Perhaps the AI's aren't performing as poorly as thought. Perhaps this is why they performed better to begin with. Otherwise, production ML systems are generally not as highly regarded as these models when they perform as significantly below test data sets in production. Some is expected, but "struggle to replicate" implies more.
>"Most tools can only diagnose abnormalities that are common in training data"
Well yes, training on novel examples is one thing. Training on something categorically different is another thing all together. Also there are thresholds of detection. Detecting nothing, or with a a lower confidence, or unknown anomaly, false positive, etc. How much of the inaccuracy isn't wrong, but simply something that is amended or expanded upon when reviewed? Some details here would be useful.
I'm highly skeptical when generalized statements exclude directly relevant information to which an is referring. The few sources provided don't at all cover model accuracy, and the primary factor cited as problematic with AI review, lack of diversity in study composition for women, ethnic variation, children, links to a a meta study that was not at all related to the composition of models and their training data sets.
The article begins as what appears to be a criticism of AI accuracy with the thinness outlined above but then quickly moves on to a "but that's not what radiologists do anyway", and provides a categorical % breakdown of time spent where Personal/Meetings/Meals and some mixture of the others combine to form at least a third that could be categorized as "Time where the human isn't necessary if graphs are being interpreted by models."
I'm not saying there aren't points here, but overall, it simply sounds like the hand-wavy meandering of someone trying to gatekeep a profession whose services could be massively more utilized with more automation, and sure-- perhaps at even higher quality with more radiologists to boot-- but perfect is the enemy of the good etc. on that score, with enormous costs and delays in service in the meantime.
Poorer performance in real hospital settings has more to do with the introduction of new/unexpected/poor quality data (i.e. real world data) that the model was not trained in or optimized for. They still do very well generally, but often do not hit equivalent performance to what is submitted to the FDA, or in marketing materials. This does not mean they aren’t useful.
Clinical AI also has to balance accuracy with workflow efficiency. It may be technically most accurate for a model to report every potential abnormality with associated level of certainty, but this may inundate the radiologist with spurious findings that must be reviewed and rejected, slowing her down without adding clinical value. More data is not always better.
In order for the model to have high enough certainty to get the right balance of sensitivity and specificity to be useful, many many examples are needed for training, and with some rarer entities, that is difficult. It also inherently reduces the value of the model it is only expected to identify its target disease 3 times/year.
That’s not to say advances in AI won’t overcome these problems, just that they haven’t, yet.
For anomaly systems like this, is it effective to invert the problem by not include the ailment/problem in the training data, then looking for a "confused" signal rather than a "x% probability of ailment" type signal?
On that, I'm not sure. My area of ML & data science practice is, thankfully, not so high-stakes. There's a method of anomaly detection called one-class SVM (Support Vector Machine) that is pretty much this- train on normal, flag on "wtf is this you never training me on this 01010##" <-- Not actual ISO standard ML model output or medical jargon. But I'm not sure if that's what's most effective here. My gut instinct in first approaching the task would be to throw a bunch of models at it, mixed-methods, with one-class SVM as a fall back. But I'm also way out of my depth on medical diagnostics ML so that's just a generalist's guess.
I find the radiologist use case an illuminating one for the adoption of AI across business today. My takeaway is that when the tools get better, radiologists aren't replaced, but take up other important tasks that sometimes become second nature when reads (unassisted) are the primary goal.
In particular, doctors appear to defer excessively to assistive AI tools in clinical settings in a way that they do not in lab settings. They did this even with much more primitive tools than we have today... The gap was largest when computer aids failed to recognize the malignancy itself; many doctors seemed to treat an absence of prompts as reassurance that a film was clean
Reminds me of the "slop" discussions happening right now. When the tools seem good, but aren't, we develop a reliance to false negatives, e.g. text that clearly "feels" written by a GPT model.
I lived this previously. The author is missing some important context.
Spray-and-Pray Algorithms
After AlexNet, dozens of companies rushed into medical imaging. They grabbed whatever data they could find, trained a model, then pushed it through the FDA’s broken clearance process. Most of these products failed in practice because they were junk.
In mammography, only 2–3 companies actually built clinically useful products.
Products actually have to be useful.
There were two products in the space: CAD, and Triage. CAD is basically overlay on the screen as you read the case. Rads hated this because it was distracting and because the feature-engineering based CAD from the 80s-90s was demonstrated to be a failure. Users basically ignored "CADs."
Triage is when you prioritize cases (cancers to the top of the stack). This has little to no value because when you have a stack of 50 cases you have to do today, then why do you care about the order? There were some niche use cases but it was largely pointless. It could actually detrimental. The algotithm would put easy cancer cases on the top, so now the user would spend less time on the rest of the stack (where the harder cases would end up).
*Side note:* did you know that using CAD was a billable extra to insurance. Even through it was proven to not work, for years it remained reimbursable up until a few years ago.
Poor Validation Standards
Models collapsed in the real world because the FDA process is designed for drugs/hardware, not adaptive software.
Validation typically = ~300 “golden” cases, labeled by 3 radiologists with majority vote arbitration.
If 3 rads say it’s cancer, it’s cancer. If they disagree, it's not a good case for the study. This filtering ignores the hard cases (where readers disagree), which is exactly what models need to handle in the real world.
Instead of 500K noisy real-world studies, you validate on a sanitized dataset. Companies learned how to “cheat” by over fitting to these toy datasets.
You can explain this to regulators endlessly, but the bureaucracy only accepts the previously blessed process.
Note: The previous process was defined by CAD, a product that was cleared in the 80s and shown to fail miserably in clinical use. This validation standard that demonstrated grand historical regulatory failure is the current standard that you MUST use for any devices that look like a CAD in mammography.
Politics Over Outcomes
We ran the largest multi-site prospective (15) trial in the space. Results:
~50% reduction in radiologist workload.
Increased cancer detection rate.
10x lower cost per study.
We even caught cancers missed in the standard workflow. Clinics still resisted adoption—because admitting missed cancers looked bad for their reputation. Bureaucratic EU healthcare systems preferred to avoid the embarrassment even through it was entirely internal.
I'll leave you with one particularly salient story. I was speaking to the head a large US hospital IT/Ops organization. We had a 30 minute conversation about how to avoid putting our software decision in the EMR/PACS so that they could avoid litigation risk. Not once did we ever talk about patient impact. Not Once...
Despite all that, our system caught cancers that would have been missed. Last I checked at least 104 women had their cancers detected by our software and are still walking around.
That’s the real win, even if politics buried the broader impact.
Every use of AI has its own problem of "person with 10 fingers" that AI image generation faces and can't seem to solve. For programmers, it's code that calls made up libraries and makes up language semantics. In prose, it's completely incoherent narratives that forget where they are going halfway through. For programmers it's making up case law and citations. Same for scientists, making up authorities and papers and results.
AI art is getting better but still it's very easy for me to quickly distinguish AI result from everything else, because I can visually inspect the artifacts and it's usually not very subtle.
I'm not a radiologist, but I would imagine AI is doing the same thing here, making up things that are cancer, missing things that aren't cancer, and it takes an expert to distinguish the false positives from true. So we're back at square one, except the expertise has shifted from interpreting the image to interpreting the image and also interpreting the AI.
> AI art is getting better but still it's very easy for me to quickly distinguish AI result from everything else, because I can visually inspect the artifacts and it's usually not very subtle.
I actually disagree in that it's not easy for me at all to quickly distinguish AI images from everything else. But I think we might differ what we mean by "quickly". I can quickly distinguish AI if I am looking. But if I'm mindlessly doomscrolling I cannot always distinguish 'random art of an attractive busty woman in generic fantasy armor that a streamer I follow shared' as AI. I cannot always distinguish 'reply-guy profile picture that's like a couple dozen pixels in dimensions' as AI. I also cannot always tell if someone is using a filter if I'm looking for maybe 5 seconds tops while I scroll.
AI art is easy to pick out when no effort was made to deviate from the default style that the models use. Where the person put in a basic prompt of the desired contents ("man freezing on a bed") and calls it a day. When some craftsmanship is applied to make it more original, that's when it gets progressively harder to catch it at first glance. Though I'd argue that it's more transformative and thus warrants less criticism than the lazy usage.
As a related aside, I've started seeing businesses clearly using ChatGPT for their logos. You can tell from the style and how much random detail there is contrasted with the fact that it's a small boba tea shop with two employees. I am still trying to organize my thoughts on that one.
The thing with medical services is that there is never enough.
If you are rich and care about your health (especially as move past age 40), you probably have use for a physiotherapist, a nutritionist, therapist, regular blood analysis, comprehensive cardio screening, comprehensive cancer screening etc. Arguably, there is no limit to the amount of medical services that people could use if they were cheap and accessible enough.
Even if AI tools add 1-2% on the diagnostic side every year, it will take a very, very long time to catch up to demand.
The only part of this article I believe is the legal and bureaucratic burdens part.
"Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians"
I've had the misfortune of dealing with a radiologist or two this year. They spent 10-20 minutes talking about the imaging and the results with me. What they said was very superficial and they didn't have answers to several of the questions I asked.
I went over the images and pathology reports with ChatGPT and it was much better informed, did have answers for my questions, and had additional questions I should have been asking. I've used ChatGPT's information on the rare occasions when doctors deign to speak with me and it's always been right. Me, repeating conclusions and observations ChatGPT made, to my doctors, has twice changed the course of my treatment this year, and the doctors have never said anything I've learned from ChatGPT is wrong. By contrast, my doctors are often wrong, forgetful, or mistaken. I trust ChatGPT way more than them.
Good image recognition models probably are much better than human radiologists already and certainly could be vastly better. One obstacle this post mentions - AI models "struggle to replicate this performance in hospital conditions", is purely a choice. If HMOs trained models on real data then this would no longer be the case, if it is now, which I doubt.
I think it's pretty clearly doctors, and their various bureaucratic and legal allies, defending their legal monopoly so they can provide worse and slower healthcare at higher prices, so they continue to make money, at the small cost of the sick getting worse and dying.
As the prediction of radiologists going dodo sprung out from improvements in image recognition, why don't we see premature and hysterically hyped predictions of psychiatrists being unemployed due to language models.
Their workday consists of conversations, questions and reading. Something LLMs more than excel at doing, tirelessly and in huge volumes.
And if radiologists are still the top bet due to image recognition being so much hotter, then why not add dermatologists to the extinction roster? They only ever look at regular light images, it should be a lower hanging fruit.
(I'm aware of the nuances that make automation of these work roles hard, I'm just trying to shine some light on the mystery of radiologists being perceived as the perennial easy target)
I personally think we are in the pre-broadband era of AI. That is, what is currently being built will contribute to some AI dot-com 1.0 bubble burst, but after that there will be some advancements in it's distribution model that will allow it to thrive and infiltrate our society at a core level. Right now it's a bunch of dreamers building the pets.com that no one asked for, but after all the competition is shaken off, there will definitely be some clear winners, and maybe an Amazon.com of sorts for AI that people will adopt.
I'm an interventional radiologist with a master's in computer science. People outside radiology don't get why AI hasn't taken over.
Can AI read diagnostic images better than a radiologist? Almost certainly the answer is (or will be) yes.
Will radiologists be replaced? Almost certainly the answer is no.
Why not? Medical risk. Unless the law changes, a radiologist will have to sign off on each imaging report. So say you have an AI that reads images primarily and writes pristine reports. The bottleneck will still be the time it takes for the radiologist to look at the images and validate the automated report. Today, radiologist read very quickly, with a private practice rads averaging maybe 60-100 studies per day (XRs, ultrasounds, MRIs, CTs, nuclear medicine studies, mammograms, etc). This is near the limit of what a human being can reasonably do. Yes, there will be slight gains at not having to dictate anything, but still having to validate everything takes nearly as much time.
Now, I'm sure there's a cavalier radiologist out htere who would just click "sign, sign, sign..." but you know there's a malpractice attorney just waiting for that lawsuit.
But this indicates lack of incentives to reduce healthcare costs by optimisation. If AI can do something well enough , and AI + humans surpass humans leading to costs reductions/ increased throughput this should be reflected in the workflows.
I feel that human processes have inertia and for lack of a better word, gatekeepers feel that new, novel approaches should be adopted slowly and which is why we are not seeing the impact, yet. Once a country with the right incentive structure (e.g. China ) can show that it can outperform and help improve the overall experience I am sure things will change.
While 10 years progress is a lot in ML, AI , in more traditional fields it probably is a blip to change this institutional inertia which will change generation by generation. All that is needed is an external actor to take the risk and show a step change improvement. Having experienced how healthcare in US I feel people are only scared to take on bold challenges
Most of your job security comes from arbitrary supply constraint and regulation. Which is another way of saying, it’s one regulation away from disappearing
Doesn't most of the stuff a radiologist does get double checked anyways by the doctor that orders the scan in the first place? I guess not a more typical screening scan like a mammogram. However, for anything else like a CT, MRI, Xray, etc. I expect the doctor/NP that ordered it in the first place will want to take a look at the image itself and not just the report on the image.
A primary physician (or NP) isn't in a position to validate the judgement of a specialist. Even if they had the training and skill (doubtful), responsibility goes up, not down. It's all a question of who is liable when things go wrong.
When Tesla demoed (via video) self-driving in 2016 with a claim "The person in the driver’s seat is only there for legal reasons. He is not doing anything. The car is driving itself" and then when they unveiled Semi in 2017 - I tweeted out and honestly thought that trucking industry is changed forever and it doesn't make sense to be starting in trucking industry. It's almost end of 2025 and either nothing out of it or just a small part of it panned out.
I think we all have become hyper-optimistic on technology. We want this tech to work and we want it to change the world in some fundamental way, but either things are moving very slowly or not at all.
Look at Waymo, not Robotaxi. Waymo is essentially the self driving vision I had as a kid, and ridership is growing exponentially as they expand. It's also very safe if you believe their statistics[0]. I think there's a saying about overestimating stuff in the short term and underestimating stuff in the long term that seems to apply here, though the radiologist narrative was definitely wrong.
[0] https://waymo.com/safety/impact/
Even though the gulf between Waymo and the next runner up is huge, it too isn't quite ready for primetime IMO. Waymos still suffer from erratic behavior at pickup/dropoff, around pedestrians, badly marked roads and generally jam on the brakes at the first sign of any ambiguity. As much as I appreciate the safety-first approach (table stakes really, they'd get their license pulled if they ever caused a fatality) I am frequently frustrated as both a cyclist and driver whenever I have to share a lane with a Waymo. The equivalent of a Waymo radiologist would be a model that has a high false-positive and infinitesimal false-negative rate which would act as a first line of screening and reduce the burden on humans.
I've seen a lot of young people (teens especially) cross active streets or cross in front of Waymos on scooters knowing that they'll stop. I try not to do anything too egregious, but I myself have begun using Waymo's conservative behavior as a good way to merge into ultra high density traffic when I'm in a car, or to cross busy streets when they only have a "yield to pedestrian" crosswalk rather than a full crosswalk. The way you blip a Waymo to pay attention and yield is beginning to move into the intersection, lol.
I always wonder if honking at a Waymo does anything. A Waymo stopped for a (very slow) pickup on a very busy one lane street near me, and it could have pulled out of traffic if it had gone about 100 feet further. The 50-ish year old lady behind it laid on her horn for about 30 seconds. Surreal experience, and I'm still not sure if her honking made a difference.
I like Waymos though. Uber is in trouble.
I honestly don't think we will have a clear answer to this question anytime soon. People will be in their camps and thats that.
Just to clarify, have you ridden in a Waymo? It didn't seem entirely clear if you just experienced living with Waymo or have ridden in it.
I tried it a few times in LA. What an amazing magical experience. I do agree with most of your assertions. It is just a super careful driver but it does not have the full common sense that a driver in a hectic city like LA has. Sometimes you gotta be more 'human' and that means having the intuition to discard the rules in the heat of the moment (ex. being conscious of how cyclists think instead of just blindly following the rules carefully, this is cultural and computers dont do 'culture').
You have to consider that the AVs have their every move recorded. Even a human wouldn't drive more aggressively under those circumstances.
Probably what will happen in the longer term is that rules of the road will be slightly different for AVs to allow for their different performance.
> Waymos still suffer from erratic behavior at pickup/dropoff, around pedestrians, badly marked roads and generally jam on the brakes at the first sign of any ambiguity.
As do most of the ridesharing drivers I interact with nowadays, sadly.
The difference is that Waymo has a trajectory that is getting better while human rideshare drivers have a trajectory that is getting worse.
Society accepts that humans make mistakes and considers it unavoidable, but there exists a much higher bar expected of computers/automation/etc. even if a waymo is objectively safer in terms of incidents per miles driven, one fatality makes headlines and adds scrutiny about “was it avoidable?”, whereas humans we just shrug.
I think the theme of this extends to all areas where we are placing technology to make decisions, but also where no human is accountable for the decision.
Society only cares about the individual and no one else. If Uber/Lyft continue to enshittify with drivers driving garbage broken down cars, drivers with no standards (ie. having just smoked weed) and ever rising rates, eventually people will prefer the Waymos.
I am a long time skeptic of self-driving cars. However, Waymo has changed that for me.
I spend a lot of time as a pedestrian in Austin, and they are far safer than your usual Austin driver, and they also follow the law more often.
I always accept them when I call an Uber as well, and it's been a similar experience as a passenger.
I kinda hate what the Tesla stuff has done, because it makes it easier to dismiss those who are moving more slowly and focusing on safety and trust.
Yeah we don't need to compare robots to the best driver or human, just the average, for an improvement.
However, like railroad safety is expensive heavily regulated, self driving car companies have the same issue.
Decentralized driving decentralizes risk.
so when I have my _own_ robot to do it, it'll be easy and cheap.
Honestly, once a traffic island city (like Singapore) or some other small nation state adopts self driving only within its limits and shows that it is much easier when all are self driving I think the opposition to the change will slowly reduce.
Rain, Snow etc. are still challenges but needs a bold bet in a place that wants to show how futuristic it is. The components are in place (Waymo cars), what is needed is high enough labor cost to justify the adoption.
I agree with both comments here. I wonder what the plausibility of fully autonomous trucking is in the next 10-30 years...
Is there any saying that exists about overestimating stuff in the near term and long term but underestimating stuff in the midterm? Ie flying car dreams in the 50s etc.
> ... but underestimating stuff in the midterm? Ie flying car dreams in the 50s etc.
We still don't have flying cars 70 years later, and they don't look any more imminent than they did then. I think the lesson there is more "not every dream eventually gets made a reality".
If it were about the costs for employees, you could ship it with the railway. That simply isn't the reason.
I remember Bill Gates said: "We overestimate what we can do in one year and underestimate what we can do in ten years".
Not Musk. He promised full autonomy within 3 years about 10 years ago.
https://en.wikipedia.org/wiki/List_of_predictions_for_autono...
Musk and Gates have very different philosophies.
Gates seems more calm and collected having gone through the trauma of almost losing his empire.
Musk is a loose cannon having never suffered the consequences of his actions (ie. early Gates and Jobs) and so he sometimes gets things right but will eventually crash and burn having not had the fortune of failing and maturing early on in his career(he is now past the midpoint of his career with not enough buffer to recover).
They are both dangerous in their own ways.
Waymo still have the ability to remotely deal with locations the AI has problems; I'd love to know what type of percentage of trips need to do that now. Having that escape together with only doing tested areas makes their job a LOT easier. (Not that it's bad - it's a great thing and I wish for it here!)
> a saying about overestimating stuff in the short term and underestimating stuff in the long term
This is exactly what came to my mind also.
Waymo is very impressive, but also demonstrates limitations of these systems. Waymo vehicles are still getting caught performing unsafe driving maneuvers, they get stuck alleys in numbers, and responders have trouble getting them to acknowledge restricted areas. I am very supportive of this technology, but also highly skeptical as long as these vehicles are directly causing problems for me personally. Driving is more than a technical challenge, it involves social communication skills that automated vehicles do not yet have.
It's limited to a few specific markets though. My bet is they aren't going to be able to roll it out widely easily. Probably need to do years of tests in each location to figure out the nuances of the places.
Yeah, I have no idea if Waymo will ever be a rural thing honestly, mostly for economic reasons. I'm skeptical it would get serious suburban usage this decade too. But for major cities where less than 80% of people own cars, test time doesn't seem to be making a difference. They've been expanding in Austin and Atlanta, seemingly with less prep time than Phoenix and San Fran.
They keep expanding in places where it doesn't snow.
They've got testing facilities in Detroit ( https://mcity.umich.edu/what-we-do/mcity-test-facility/ ) ... but I want to see it work while it is snowing or after it has snowed in the upper midwest.
https://youtu.be/YvcfpO1k1fc?si=hONzbMEv22jvTLFS - has suggestions that they're starting testing.
If AI driving only works in California, New Mexico, Arizona, and Texas... that's not terribly useful for the rest of the country.
If I were in charge of Waymo, I’d roll out in snowy places last. The odds of a “couldn’t be avoided” accident is much higher in snow/ice. I’d want an abundance of safety data in other places to show that the cars are still safe, and it was the snow instead of the tech that caused the accident.
Define the rest of the country?
If you refer to rural areas, thats 1/7 of the population and ~10% of GDP. They can be tossed aside like they are in other avenues.
They're testing in Denver and NYC so its coming.
Atlanta seems to be a bit contradictory to some of your other thoughts.
The city itself is relatively small. A vast majority of area population lives distributed across the MSA, and it can create hellish traffic. I remember growing up thinking 1+ hour commutes were just a fact of life for everyone commuting from the suburbs.
Not sure what car ownership looks like, and I haven’t been in years, but I’d imagine it’s still much more than just 20%
Austin is also a car city, everyone has a car there. Public transit in Austin is a joke, and Waymo can't get on the highway so it's only useful for getting back to your hotel from Rainey Street, and maybe back to your dorm from the Drag, but nobody is using Waymo to commute from Round Rock
I could see it taking off in the suburbs/rural areas if they start having a franchise model when it’s more mature.
I saw this timeline a while ago: https://www.reddit.com/r/waymo/s/mSm0E3yYTY that shows their timeline in each city. Shows Atlanta at just over a year. I think once they've handled similar cities it gets easier and easier to add new ones.
No it’s just machine learning was always awesome for the 98% of the cases. We got fooled that we can easily deal with the remaining 2%.
It is the usual complexity rule of software: solving 80% of the problem is usually pretty easy at only takes about 50% of the estimated effort, it is the remaining 20% that takes up the remaining 90% of estimated effort (thus the usual schedule overruns).
The interesting thing is that there are problems for which this rule applies recursively. Of the remaining 20%, most of it is easier than the remaining 20% of what is left.
Most software ships without dealing with that remaining 20%, and largely that is OK; it is not OK for safety critical systems though.
well part of the reason why you may have felt mislead by that video is because it was staged so i wouldn't feel that bad.
https://www.reuters.com/technology/tesla-video-promoting-sel...
for me i have been riding in waymos the last year and have been very pleased with the results. i think we WANT this technology to move faster but the some of the challenges at the edges take a lot of time and resources to solve, but not fundamentally unsolvable.
Waymo is a 21 year old company that only operates on a small part of the US after $10 billions of funding.
it's also widely believed that the cars are remotely operated, not autonomous.
they are likely semi autonomous, which is still cool, but I wish they'd be honest about it
They are:
Much like phone-a-friend, when the Waymo vehicle encounters a particular situation on the road, the autonomous driver can reach out to a human fleet response agent for additional information to contextualize its environment. The Waymo Driver does not rely solely on the inputs it receives from the fleet response agent and it is in control of the vehicle at all times. As the Waymo Driver waits for input from fleet response, and even after receiving it, the Waymo Driver continues using available information to inform its decisions. This is important because, given the dynamic conditions on the road, the environment around the car can change, which either remedies the situation or influences how the Waymo Driver should proceed. In fact, the vast majority of such situations are resolved, without assistance, by the Waymo Driver.
https://waymo.com/blog/2024/05/fleet-response/
Although I think they overstate the extent to which the Waymo Driver is capable of independent decisions. So, honest, ish, I guess.
After learning that the Amazon Go store was power by hundreds of people watching video because the AI could not handle it was a real eye opener for me.
Is this why Waymo is slow to expand, not enough remote drivers?
Maybe that is where we need to be focused, better remote driving?
> Maybe that is where we need to be focused, better remote driving?
I think maybe we can and should focus on both. Better remote driving can be extended into other equipment operations as well - remote control of excavators and other construction equipment. Imagine road construction, or building projects, being able to be done remotely while we wait for better automation to develop.
Is that true? Nearly everything online argues against that
Waymo employees downvoting you. Pathetic.
> thought that trucking industry is changed forever
What I find really crazy is that most trains are still driven by humans.
Most work is actually in oversight and getting the train to run when parts fail. When running millions of machines 24/7 there is always a failing part. Also understanding gesticulation humans and running wildlife is not yet (fully) automatable.
only 2 people (engineer and conductor) for an entire train that is over a mile long seems about right to me though
Much of that is about union power more than tech maturity.
I think it's the matter of scale. Way more truck drivers than locomotive engineers.
I realized long ago that full unattended self driving requires AGI. I think Elon finally figured that out. So now LLMs are going to evolve into AGI any moment. Um no. Tesla (and others) have effectively been working on AGI for 10 years with no luck
> I realized long ago that full unattended self driving requires AGI.
You can do 99% of it without AGI, but you do need it for the last 1%.
Unfortunately, the same is true for AGI.
> I realized long ago that full unattended self driving requires AGI.
Yikes.
I recommend you take some introductory courses on AI and theory of computation.
You should either elaborate on your argument, or at least provide further reading that clarifies your point of contention. This kind of low effort nerd-sniping contributes nothing.
GP's statement is completely unsupported, the burden is on them.
It's commonly brought up saying, and I don't think it's too far from the truth.
Driving under every condition requires a very deep level of understanding of the word. Sure, you can get to like 60% by a simple robot vacuum logic, and to like 90% with what e.g. Waymo does. But the remaining 10% is crazy complex.
What about a plastic bag floating around on a highway? The car can see it, but is it an obstacle to avoid? Should it slam the brakes? And there are a bunch of other extreme examples (what about a hilly road on a Greek island where people just honk to notify the other side that they are coming, without seeing them?)
Responding to ridiculous uncited wild comments doesn't require a phd thesis paper, my friend.
> I realized long ago that full unattended self driving requires AGI.
Not even close.
The vast majority of people have a small number of local routes completely memorized and do station keeping in between on the big freeways.
You can see this when signage changes on some local route and absolute chaos ensues until all the locals re-memorize the route.
Once Waymo has memorized all those local routes (admittedly a big task), it's done.
So waymo has AGI?
Meanwhile, it’s my feeling that technology is moving insanely fast but people are just impatient. You move the bar and the expectations move with it. I think part of the problem is that the market rewards execs who set expectations beyond reality. If the market was better at rewarding outcomes not promises, you’d see more reasonable product pitches.
How have expectations moved on self driving cars? Yes, we're finally getting there, but adoption is still tiny relative to the population and the cars that work best (Waymo) are still humongously expensive + not available for consumer purchase.
80/20 rule might be 99/1 for AI.
"I think we all have become hyper-optimistic on technology. We want this tech to work and we want it to change the world in some fundamental way, but either things are moving very slowly or not at all."
Who is "we"? The people who hype "AI"?
It's almost end of 2025 and either nothing out of it or just a small part of it panned out.
The truck part seems closer than the car part.
There are several driverless semis running between Dallas, Houston, and San Antonio every day. Fully driverless. No human in the cab at all.
Though, trucking is an easier to solve problem since the routes are known, the roads are wide, and in the event of a closure, someone can navigate the detour remotely.
This story (the demand for Radiologists) really shows a very important thing about AI: It's great when it has training data, and bad at weird edge cases.
Gee, seems like about the worst fucking thing in the world for diagnostics if you ask me, but what do I know, my degree is in sandwiches and pudding.
It's not about optimism. It is well established in the industry that Tesla's hardware-stack gives them 98% accuracy at the very most. But those voices are drowned by the marketing bravado.
In the case of Musk it has worked out. His lies have earned him a fortune and now he asks Tesla to pay him out with a casual 1 trillion paycheck.
This is such a stereotypical SF / US based perspective.
Easy to forget the rest of the world does not and never has ticked this way.
Don't get me wrong, optimism and thinking of the future are great qualities we direly need in this world on the one hand.
On the other, you can't outsmart physics.
We've conquered the purely digital realm in the past 20 years.
We're already in the early years of the next phase were the digital will become ever more multi-modal and make more inroads into the physical world.
So many people bring an old mindset to a new context, where maring of errors, cost of mistakes or optimizing the last 20% of a process is just so vastly different than a bit of HTML, JS and backend infra.
Fundamental change does indeed happen very slowly. But it does happen.
The universe has a way with being disappointing. This isn't to say that life is terrible and we should have no optimism. Rather, that things generally work out for the better, but usually not in the way we'd prefer them to.
Waymo has worked out. I’ve taken one so many times now I don’t even think about it. If Waymo can pull this off in NYC I believe it will absolutely be capable of long distance trucking not that far in the future.
Trucks are orders of magnitude more dangerous. I wouldn’t be surprised if Waymo is decades away from being able to operate a long haul truck on the open interstate.
For trucking I think self driving can be, in the short term, an opportunity for owner-operators. An owner-operator of a conventional truck can only drive one truck at a time, but you could have multiple self driving trucks in a convoy led by a truck manned by the owner-operator. And there might be an even greater opportunity for this in Europe thanks to the low capacity of European freight rail compared to North America.
I used to think this sort of thing too. Then a few years ago I worked with a SWE who had experience in the trucking industry. His take was that most trucking companies are too small scale to benefit from this. The median trucking operation is basically run by the owner's wife in a notebook or spreadsheet- and so their ability to get the benefits of leader/follower mileage like that just doesn't exist. He thought that maybe the very largest operators- Walmart and Amazon- could benefit from this, but he thought that no one else could.
This was why he went into industrial robotics instead, where it was clear that the finances could work out today.
Yeah, I guess the addressable market of “truck owners who can afford to buy another truck but not hire another driver” might be smaller than I thought.
Trucks are harder. The weight changes a lot, they are off grid for huge stretches, mistakes are more consequential.
It's also like nobody learns from the previous hype cycles. Short term overly optimistic predications followed by disillusionment and then long term benefits which deliver on some of the early promises.
For some reason, enthusiasts always think this time is different.
The best story I heard about machine learning and radiology was when folks were racing to try to detect COVID in lung X-rays.
As I recall, one group had fairly good success, but eventually someone figured out that their data set had images from a low-COVID hospital and a high-COVID hospital, and the lettering on the images used different fonts. The ML model was detecting the font, not the COVID.
[a bit of googling later...]
Here's a link to what I think was the debunking study: https://www.nature.com/articles/s42256-021-00338-7
If you're not at a university, try searching for "AI for radiographic COVID-19 detection selects shortcuts over signal" and you'll probably be able to find an open-access copy.
I remember a claim that someone was trying to use an ML model to detect COVID by analyzing the sound of the patient coughing.
I couldn't for the life of me understand how this was supposed to work. If the coughing of COVID patients (as opposed to patients with other respiratory illnesses) actually sounds meaningfully different in a statistically meaningful way (and why did they suppose that it would? Phlegm is phlegm, surely), surely a human listener would have been able to figure it out easily.
Anecdotes like this are informative as far as they go, but they don't say anything at all about the technique itself. Like your story about the fonts used for labeling, essentially all of the drawbacks cited by the article come down to inadequate or inappropriate training methods and data. Fix that, which will not be hard from a purely-technical standpoint, and you will indeed be able to replace radiologists.
Sorry, but in the absence of general limiting principles that rule out such a scenario, that's how it's going to shake out. Visual models are too good at exactly this type of work.
The issue is that in medicine, much like automobiles, unexpected failure modes may be catastrophic to individual people. “Fixing” failure modes like the above comment is not difficult from a technical standpoint, that’s true, but you can only fix it once you’ve identified it, and at that point you may have a dead person/people. That’s why AI in medicine and self driving cars are so unlike AI for programming or writing and move comparatively at a snails pace.
Yet self-driving cars are already competitive with human drivers, safety-wise, given responsible engineering and deployment practices.
Like medicine, self-driving is more of a seemingly-unsolvable political problem than a seemingly-unsolvable technical one. It's not entirely clear how we'll get there from here, but it will be solved. Would you put money on humans still driving themselves around 25-50 years from now? I wouldn't.
These stories about AI failures are similar to calling for banning radiation therapy machines because of the Therac-25. We can point and laugh at things like the labeling screwup that pjdesno mentioned -- and we should! -- but such cases are not a sound basis for policymaking.
> Yet self-driving cars are already competitive with human drivers, safety-wise, given responsible engineering and deployment practices.
Are they? Self driving cars only operate in a much safer subset of conditions that humans do. They have remote operators who will take over if a situation arises outside of the normal operating parameters. That or they will just pull over and stop.
Telsa told everybody 10 years ago self driving cars were a reality.
Waymo claims to have it. Some hackernews comenters too, I started to belive those are Waymo employees or stock owners.
Apart from that I know nobody that has even use or even seen a self driving car.
Self-driving cars are not a thing so you can't say they are more realible than humans.
When it weren't for the font it might be anomalies in the image taking or even in the encoder software. You can never really be sure, what exactly the ML is detecting.
Exactly. A marginally higher image ISO at one location vs a lower ISO at another could potentially have a similar effect, and it would be quite difficult to detect.
Why not? That's what Grad-CAM is for right?
What if the ML takes the conclusion exactly from the right pixels, but the cause is a rasterization issue.
You can give it the same tests the human radiologists take in school.
They do take tests, don't they?
They don't all score 100% every time, do they?
The point here is that the radiologists has a concept of knowing which light patterns are sensible to draw conclusions from and which not, because the radiologist has a concept of real world 3D objects.
> Three things explain this. First,... Second, attempts to give models more tasks have run into legal hurdles: regulators and medical insurers so far are reluctant to approve or cover fully autonomous radiology models. Third, even when they do diagnose accurately, models replace only a small share of a radiologist’s job. Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
Everything else besides the above in TFA is extraneous. Machine learning models could have absolute perfect performance at zero cost, and the above would make it so that radiologists are not going to be "replaced" by ML models anytime soon.
I only came to this thread to say that this is completely untrue:
>Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
The vast majority of radiologists do nothing other than: come in (or increasingly, stay at home), sit down at a computer, consume a series of medical images while dictating their findings, and then go home.
If there existed some oracle AI that can always accurately diagnose findings from medical images, this job literally doesn't need to exist. It's the equivalent of a person staring at CCTV footage to keep count of how many people are in a room.
Agreed, I'm not sure where the OP from TFA is working but around here, radiologists have all been bought out and rolled into Radiology As A Service organizations. They work from home or at an office, never at a clinic, and have zero interactions with the patient. They perform diagnosis on whatever modality is presented and electronically file their work into their EMR. I work with a couple such orgs on remote access and am familiar with others, it might just be a selection bias on my side but TFA does not reflect my first-hand experience in this area.
Interesting - living near a large city, all of the radiologists I know work for hospitals, spending more of their day in the hospital reading room versus home, including performing procedures, even as diagnostic radiologists.
I think it may be selection bias.
My wife is an ER doctor. I asked her and she said she talks to the radiologists all the time.
I also recently had surgery and the surgeon talked to the radiologist to discuss my MRI before operating.
What the article suggests is backed up by research, at least in hospital settings: https://www.jacr.org/article/S1546-1440(13)00220-2/abstract
Are these the ones making 500K? Sounds like more of an assistance job than an MD.
Radiologists are often the ones who are the "brains" of medical diagnosis. The primary care or ER physician gets the patient scanned, and the radiologist scrolls through hundreds if not thousands of images, building a mental model of the insides of the patient's body and then based on the tens of thousands of cases they've reviewed in the past, as well as deep and intimate human anatomical knowledge, attempts to synthesize a medical diagnosis. A human's life and wellness can hinge on an accurate diagnosis from a radiologist.
Does that sounds like an assistance's job?
Makes sense. Knowing nothing about it, I was picturing a tech sitting at home looking at pictures saying "yup, there's a spot", "nope, no spot here".
For this job a decade of studies would be a bit wasteful though.
>consume a series of medical images while dictating their findings, and then go home.
In the same fashion as construction worker just shows up, "performs a series of construction tasks", then go home. We just need to make a machine that performs "construction tasks" and we can build cities, railways and road networks for nothing but the cost of the materials!
Perhaps this minor degree of oversimplification is why the demise of radiologists have been so frequently predicted?
If they had absolute perfect performance at zero cost, you would not need a radiologist.
The current "workflow" is primary care physician (or specialist) -> radiology tech that actually does the measurement thing -> radiologist for interpretation/diagnosis -> primary care physician (or specialist) for treatment.
If you have perfect diagnosis, it could be primary care physician (or specialist) -> radiology tech -> ML model for interpretation -> primary care physician (or specialist.
If we're talking utopian visions, we can do better than dreaming of transforming unstructured data into actionable business insights. Let's talk about what is meaningfully possible: Who assumes legal liability? The ML vendor?
PCPs don't have the training and aren't paid enough for that exposure.
Nope.
To understand why, you would really need to take a good read of the average PCP's malpractice policy.
The policy for a specialist would be even more strict.
You would need to change insurance policies before your workflow was even possible from a liability perspective.
Basically, the insurer wants, "a throat to choke", so to speak. Handing up a model to them isn't going to cut it anymore than handing up Hitachi's awesome new whiz-bang proton therapy machine would. They want their pound of flesh.
In that scenario, the "throat to choke" would be the primary care physician. We won't think of it as an "ML radiologist", just as getting some kind of physical test done and bringing it to the doctor for interpretation.
If you're getting a blood test, the pipeline might be primary care physician -> lab with a nurse to draw blood and machines to measure blood stuff -> primary care physician to interpret the test results. There is no blood-test-ologist (hematologist?) step, unlike radiology.
Anyway, "there's going to be radiologists around for insurance reasons only but they don't bring anything else to patient care" is a very different proposition from "there's going to be radiologists around for insurance reasons _and_ because the job is mostly talking to patients and fellow clinicians".
Doesnt this become the developer? Or perhaps a specialist insurer who develops expertise and experience to indemnify them?
Oh that could indeed happen in that hypothetical timeline. But in that timeline the developer would be paying the malpractice premium.
And it would be the developer's throat that gets choked when something goes awry.
I'm betting developers will want to take on neither the cost of insurance, nor the increased risk of liability.
Let’s suppose I go to the doctor and get tested for HIV. There isn’t a specialist staring at my blood through a microscope looking for HIV viruses, they put my blood in a machine and the machine tells them, positive or negative. There is a false positive rate and a false negative rate for the test. There’s no fundamental reason you couldn’t put a CT scan into a machine the same way.
Pretty much everything has false positives and false negatives. Everything can be reduced to this.
Human radiologists have them. They can miss things: false negative. They can misdiagnose things: false positive.
Interviews have them. A person can do well, be hired and turn out to be bad employee: false positive. A person who would have been a good employee can do badly due to situational factors and not get hired: false negative.
The justice system has them. An innocent person can be judged guilty: false positive. A guilty person can be judged innocent: false negative.
All policy decisions are about balancing out the false negatives against the false positives.
Medical practice is generally obsessed with stamping out false negatives: sucks to be you if you're the doctor who straight up missed something. False positives are avoided as much as possible by defensive wording that avoids outright affirming things. You never say the patient has the disease, you merely suggest that this finding could mean that the patient has the disease.
Hiring is expensive and firing even more so depending on jurisdiction, so corporations want to minimize false positives as much as humanly possible. If they ever hire anyone, they want to be sure it's absolutely the right person for them. They don't really care that they might miss out on good people.
There are all sorts of political groups trying to tip the balance of justice in favor of false negatives or false positivies. Some would rather see guilty go free than watch a single innocent be punished by mistake. Others don't care about innocents at all. I could cite some but it'd no doubt lead to controversy.
They didn’t say there wouldn’t need to be change related to insurance. They obviously mean that, change included, a perfect model would move to their described workflow (or something similar).
HackerNews is often too quick to reply with a “well actually” that they miss the overall point.
>Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
How often do they talk to patients? Every time I have ever had an x-ray, I have never talked to a radiologist. Fellow clinicians? Train the xray tech up a bit more.
If the mote is 'talking to people' that is a mote that doesn't need an MD, or at least not a full specialization MD. ML could kill radiologist MD, radiologist could become the job title of a nurse or x-ray tech specialized in talking to people about the output.
I don’t think they talk to patients all that often but my wife is an ER doctor and she says she talks to them all the time.
Train the xray tech up a bit more.
That's fine. But then the xray tech becomes the radiologist, and that becomes the point in the workflow that the insurer digs out the malpractice premiums.
In essence, your xray techs would become remarkably expensive. Someone is talking to the clinicians about the results. That person, whatever you call them, is going to be paying the premiums.
As a patient I don't think I've ever even talked to any radiologist that actually analyzed my imaging. Most of the times my family or I have had imaging done the imaging is handled by a tech who just knows how to operate the machines while the actual diagnostic work gets farmed out to remote radiologists who type up an analysis. I don't even think the other doctors I actually see ever directly talk to those radiologists.
Is this uncommon in the rest of the US?
No, that is the norm. Radiologists speak with their colleagues the most, and patients rarely
It really depends on the specifics of the clinical situation; for a lot of outpatient radiology scenarios the patient and radiologist don't directly interact, but things can be different in an inpatient setting and then of course there are surgical and interventional radiology scenarios.
In 2016, Geoffrey Hinton – computer scientist and Turing Award winner – declared that ‘people should stop training radiologists now’.
If we had followed every AI evengelist sugestion the world would have collapsed.
Look, if we were okay with tolerating less regulation in medicine, and dismantled AMA, Hinton would have proven to be right by now and everyone would have been happier
People love to bring this up, and it was a silly thing to say -- particularly since he didn't seem to understand that radiologists only spend a small part of their time reading scans.
But he said it in the context of a Q&A session that happened to be recorded. Unless you're a skilled politician who can give answers without actually saying anything, you're going to say silly things once in a while in unscripted settings.
Besides that, I'd hardly call Geoffrey Hinton an AI evangelist. He's more on the AI doomer side of the fence.
No, this was not an off-hand remark. He made a whole story comparing the profession to the coyote from road runner “they’ve already run of the cliff but don’t even realize it”. It was callous, and showed a total ignorance of the fact that medicine might be more than pixel classification.
Radiologists, here, mostly sit at home, read scan and dictate reports. They rarely talk to other doctors and talking to a patient is beyond them. They are some of the specialists with the best salary.
With interventional radiologists and radio-oncologists it's different but were talking about radiologists here...
I'm a radiologist and spend 50% of my time either talking to patients or other clinicians.
You practice in Québec ? If so I am quite surprised, because my wife had a lot of scans and we never met a radiologists who wasn't a radio-oncologist. And her oncologist never talked with the radiologists either. The communication between them was always through written demands and reports. And the situation is similar between her neurologist and the radiologists.
By the way, even if I sound dismissive I have great respect for the skills required by your profession. Reading an IRM is really hard when you have the radiologist report in hand and to my untrained eyes it's impossible without it!
And since you talk to patients frequently, I have an even greater respect of you as a radiologist.
My wife’s an ER doctor and she talks to radiologists all the time.
I also recently had surgery and the surgeon consulted with the radiologist that read my MRI before operating.
Then it's an organizational problem (or choice) in the specific hospital where my wife is treated/followed and I apologize to all radiologists that actually talk to peoples in a professional capacity!
Or maybe it's related to socialized Healthcare because in the article there is a breakdown of the time spent by a radiologists in Vancouver and talking to patients isn't part of it.
I would argue an "AI doomer" is a negatively charged type of evangelist. What the doomer and the positive evangelist have in common is a massive overestimation of (current-gen) AI's capabilities.
It's the power of confidence and credentials in action. Which is why you should, when possible, look at the underlying logic and not just the conclusion derived from it. As this catches a lot of fluff that would otherwise be Trojan-Horsed into your worldview.
Many of us have changed opinions after seeing how the tech does not scale.
At the time? I would say he was a AI evangelist.
The tech scales, but accessing the training data is a real problem. It's not like scraping the whole internet. And most of it is unlabeled.
I think in general this lack affects almost all areas of human endeavor. All my speech teaching my kids how to think clearly, to young software engineers about how to build software in a some giant ass bureaucracy, how to debug some tricky problem, none of that sort of discovering truth one step at a time or teaching new stuff is in blogs or anything outside the moment.
When I do write something up, it is usually very finalized at that time; the process of getting to that point is not recorded.
The models maybe need more naturalistic data and more data from working things out.
If you need more data to scale, and there is no data, it literally can't scale.
Scale is not always about trougput. You can be constrained by many things, in this case, data.
Let's assume the last person that entered their radiologist training started then and the training lasts 5 years. At the end of their training the year is 2021 and they are around 31. So that means they will practice medicine for cca 30 years which would put the calendar at around 2051. I'd wager in 25 years we'd get there so I think his opinion still has a large percentage of being correct.
And if it doesn't work out ?
People can't tell what they'll eat next sunday but they'll predict AGI and singualrity in 25 years. It's comfy because 25 years seems like a lot of time, it isn't.
https://en.wikipedia.org/wiki/List_of_predictions_for_autono...
> I'd wager in 25 years we'd get there so I think his opinion still has a large percentage of being correct.
What percent, and which maths and facts let you calculate it ? The only percent you can be sure about is that it's 100% wishful thinking
Let’s say we do manage to develop a model that can replace radiologists in 20 years. But we stop training them today. What happens 15 years from now when we don’t have nearly enough radiologists.
Radiologists can retrain to do something else adjacent surely? Not like they'll suddenly be like an 18 year old with no degree trying to find a job.
Why do we assume that radiologists would have literally 0% involvement in the radiology workflow?
I could see the assumption that one radiologist supervises a group of automated radiology machines (like a worker in an automated factory). Maybe assume that they'd be delegated to an auditing role. But that they'd go completely extinct? There's no evidence of, even historically, a service being consumed that has zero human intervention.
> I'd wager
Maybe don't?
So all the current radiologists are going to live until 2051?
Even Marie Curie would have.
Why you write 2021? It clearly says 2016.
I mean if you change the data to fit your argument you will always make it look correct.
Lets assume we stop in 2016 like he said, where do we get the 1000 radiologist the US needs a year?
> the training lasts 5 years. At the end of their training the year is 2021
The training lasts 5 years, 2021 - 5 = 2016 If they stopped accepting people into the radiologist program but let people already in to finish, then you would stop having new radiologist in 2021.
Residents are working doctors, so we’d start losing useful work the year we stop taking new residents.
Training is a lot longer than that in Québec, radiology is a specialty, so they must first do their 5 years in medicine, followed by a 5 year diagnostic radiology residency program. And it's frequently followed by a 2 years fellowship.
So 5 + 5 + [0,2] is [10,12] years of training.
'people should stop training radiologists now'
That sentence and what you wrote are not 100% the same.
Too damn hard to predict the future! We live in an age where 20 years is unseeable for a lot of things.
In May earlier this year, the New York Times had a similar article about AI not replacing radiologists: https://archive.is/cw1Zt
It has similar insights, and good comments from doctors and from Hinton:
“It can augment, assist and quantify, but I am not in a place where I give up interpretive conclusions to the technology.”
“Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.”
Dr. Hinton agrees. In retrospect, he believes he spoke too broadly in 2016, he said in an email. He didn’t make clear that he was speaking purely about image analysis, and was wrong on timing but not the direction, he added.
The obvious answer is regulation and legal risk. It's the same reason retail pharmacists still have jobs despite their currently being slow poor-quality vending machines.
As a doctor and full stack engineer, I would never go into radiology or seek further training in it. (obviously)
AI is going to augment radiologists first, and eventually, it will start to replace them. And existing radiologists will transition into stuff like interventional radiology or whatever new areas will come into the picture in the future.
>AI is going to augment radiologists first, and eventually, it will start to replace them.
I am a medical school drop-out — in my limited capacity, I concur, Doctor.
My dentist's AI has already designed a new mouth for me, implants &all ("I'm only doing 1% of the finish-work: whatever the patient says doesn't feel just quite right, yet"—myDMD). He then CNCs in-house on his $xxx,xxx 4-axis.
IMHO: Many classes of physicians are going to be reduced to nothing more than malpractice-insurance-paying business owners, MD/DO. The liability-holders, good doctor.
In alignment with last week's (H)(1)(b) discussion, it's interesting to note that ~30% of US physician resident "slots" (<$60kUSD salary) are filled by these foreigner visa-holders (so: +$100k cost per applicant, amortized over a few years of training, each).
As a radiologist and full stack engineer, I’m not particularly worried about the profession going away. Changing, yes, but not more so than other medical or non-medical careers.
What’s your take on pharmacists? To my naive eyes, that seems like a certainty for replacement. What extra value does human judgement bring to their work?
My wife is a clinical pharmacist at a hospital. I am a SWE working on AI/ML related stuff. We've talked about this a lot. She thinks that the current generation of software is not a replacement for what she does now, and finds the alerts they provide mostly annoying. The last time this came up, she gave me two examples:
A) The night before, a woman in her 40's came in to the ER suffering a major psychological breakdown of some kind (she was vague to protect patient privacy). The Dr prescribed a major sedative, and the software alerted that they didn't have a negative pregnancy test because this drug is not approved for pregnant women and so should not be given. However, in my wife's clinical judgement- honed by years of training, reading papers, going to conferences, actual work experience and just talking to colleagues- the risk to a (potential) fetus from the drug was less than the risk to a (potential) fetus from mom going through an untreated mental health episode and so she approved the drug and overrode the alert.
B) A prescriber had earlier in that week written a script for Tylenol to be administered "PR" (per-rectum) rather than PRN (per requisite need). PR Tylenol is a perfectly valid thing that is sometimes the correct choice, and was stocked by the hospital for that reason. But my wife recognized that this wasn't one of the cases where that was necessary, and called the nurse to call the prescriber to get that changed so the nurse wouldn't have to give them a Tylenol suppository. This time there were no alerts, no flags from the software, it was just her looking at it and saying "in my clinical judgement, this isn't the right administration for this situation, and will make things worse".
So someone- with expensively trained (and probably licensed) judgement- will still need to look over the results of this AI pharmacist and have the power to override its decisions. And that means that they will need to have enough time per case to build a mental model of the situation in their brain, figure out what is happening, and override if necessary. And it needs to be someone different from the person filling out the Rx, for Swiss cheese model of safety reasons.
Congratulations, we've just described a pharmacist.
> And it needs to be someone different from the person filling out the Rx, for Swiss cheese model of safety reasons.
This is something I question. If you go to a specialist, and the specialist judges that you need surgery, he can just schedule and perform the surgery himself. There’s no other medical professional whose sole job is to second-guess his clinical judgment. If you want that, you can always get a second opinion. I have a hard time buying the argument that prescription drugs always need that second level of gatekeeping when surgery doesn’t.
So, the main reason for the historical separation (in the European tradition) between doctor and pharmacist was profit motive- you didn't want the person prescribing to have a financial stake in their treatment, else they will prescribe very expensive medicine for all cases. And surgeons in particular do have a profit motive- they are paid per service- and it is well known within the broader medical community that surgeons will almost always choose to cut. And we largely gate-keep this with the primary care physician providing a recommendation to the specialist. The PCP says "this may be something worth treating with surgery" when they recommend you go see a specialist rather than prescribing something themselves, and then the surgeon confirms (almost always).
That pharmacists also provide a safety check is a more modern benefit, due to their extensive training and ability to see all of the drugs that you are on (while a specialist only knows what they have prescribed). And surgeons also have a team to double-check them while they are operating, to confirm that they are doing the surgery on the correct side of the body, etc. Because these safety checks are incredibly important, and we don't want to lose them.
I am a pharmacist who dabbles in web dev. We should easily be replaced because all of our work on checking pill images and drug interactions are actually already automated, or the software already tells us everything.
If every doctor agreed to electronically prescribe (instead of calling it in, or writing it down) using one single standard / platform / vendor, and all pharmacy software also used the same platform / standard, then our jobs are definitely redundant.
I worked at a hospital where basically doctors and pharmacists and nurses all use the same software and most of the time we click approve approve approve without data entry.
Of course we also make IVs and compounds by hand, but that's a small part of our job.
I'm not a doc or a pharmacist (though I am in med school) and I'm sure there are areas that AI could do some of a pharmacists job but on the outpatient side they do things like answering questions for patients and helping them interpret instructions that I don't think we want AI to do... or at least I really doubt an AIs ability to gauge how well someone is understanding instructions and augment how it explains something based on that assessment... on the inpatient side, I have seen pharmacists help physicians grapple with the pros and cons of certain treatments and make judgement calls about dosing that I think it would be hard to trust an AI to do because there is no "right" answer really. It's about balancing trade offs.
IDK, these are just limitations - people that really believe in AI will tell you there is basically nothing it can't do... eventually. I guess it's just a matter of how long you want to wait for eventually to come.
I work on a kiosk (MedifriendRx) which, to some degree "replaces" pharmacists and pharmacy staff.
The kiosk is placed inside of a clinic/hospital setting, and rather than driving to the pharmacy, you pick up your medications at the kiosk.
Pharmacists are currently still very involved in the process, but it's not necessarily for any technical reason. For example, new prescriptions are (by most states' boards of pharmacies) required to have a consultation between a pharmacist and a patient. So the kiosk has to facilitate a video call with a pharmacist using our portal. Mind you, this means the pharmacist could work from home, or could queue up tons of consultations back to back in a way that would allow one pharmacist to do the work of 5-10 working at a pharmacy, but they're still required in the mix.
Another thing we need to do for regulatory purposes is when we're indexing the medication in the kiosk, the kiosk has to capture images of the bottles as they're stocked. After the kiosk applies a patient label, we then have to take another round of images. Once this happens, this will populate in the pharmacist portal, and a pharmacist is required to take a look at both sets of images and approve or reject the container. Again, they're able to do this all very quickly and remotely, but they're still required by law to do this.
TL;DR I make an automated dispensing kiosk that could "replace" pharmacists, but for the time being, they're legally required to be involved at multiple steps in the process. To what degree this is a transitory period while technology establishes a reputation for itself as reliable, and to what degree this is simply a persistent fixture of "cover your ass" that will continue indefinitely, I cannot say.
Pharmacists are not going to be replaced, their jobs like most other jobs touched by AI will evolve, possibly shrink in demand but won't completely dissapear. AI is a tool that some professional has to use after all.
There's a number of you (engineer + doctor), though quite rare. I have a few friends who are engineers as well as doctors. You're like unicorns in your field. The Neo and Morpheus of the medical industry - you can see things and understand things that most people cant in your typical field (medicine). Kudos to you!
This was actually my dream career path when I was younger. Unfortunately there's just no way I would have afforded the time and resources to pursue both, and I'd never heard of Biomedical Engineering where I grew up.
I feel like I keep running into your comments on HN. There are dozens of us!
As a doctor and full stack engineer you’d have a perfect future ahead of you in radiology - the profession will not go away, but will need doctors who can bridge the full medical-tech range
I wouldn't trust a non-radiologist to safely interpret the results of an AI model for radiology, no matter how well that model performs in benchmarks.
Similar to how a model that can do "PhD-level research" is of little use to me if I don't have my own PhD in the topic area it's researching for me, because how am I supposed to analyze a 20 page research report and figure out if it's credible or not?
The notion of “PhD-level research” is too vague to be useful anyways. Is it equivalent to a preprint, a poster, a workshop paper, a conference paper, a journal submission, or a book? Is it expected to pass peer review in a prestigious venue, a mid-tier venue, or simply any venue at all?
There’s wildly varying levels of quality among these options, even though they could all reasonably be called “PhD-level research.”
I'm a professor who trains PhDs in cryptography, and I can say that it genuinely does have knowledge equivalent to a PhD student. Unfortunately I've never gotten it to produce a novel result. And occasionally it does frightening stuff, like swapping the + and * in a polynomial evaluation when I ask it to format a LaTeX algorithm.
Why, ask another deep research model to critique it of course! ;-)
What we need is a mandate for AI transformation of Radiology: Radiologists must be required to use AI every day on X% of scans, their productivity must double with the use of AI or they'll get fired, etc. To quote CEOs everywhere: AI is a transformative technology unlike any we've ever seen in our careers, and we must embrace it in a desperate FOMO way, anything else is unacceptable.
I can't even tell if it's sarcasm anymore
> we must embrace it in a desperate FOMO way
It's clearly satire with the little jabs like this.
This article is pretty good. My current work is transitioning CV models in a large, local hospital system to a more unified deployment system, and much of the content aligns with conversations we have with providers, operations, etc..
I think the part that says models will reduce time to complete tasks and allow providers to focus on other tasks is on point in particular. For one CV task, we’re only saving on average <30min of work per study, so it isn’t massive savings from a provider’s perspective. But scaled across the whole hospital, it’s huge savings
>reduce time to complete tasks and allow providers to focus on other tasks
Or, far more likely, to cut costs and increase profits.
The demand for horses was also at an all time high when the Model T Ford was introduced.
At the risk of posting memes: https://pbs.twimg.com/media/GSXAMJRXkAA-cgI.jpg
Updated for 2025: https://i.imgflip.com/a5ywre.jpg
Somehow related: Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study
see https://news.ycombinator.com/item?id=44934207
Oh yes it is. I have worked on projects where highly trained specialized doctors have helped train the models (or trained them themselves) to catch random very difficult to notice conditions via radiology. Some of these systems are deployed at different hospitals and medical facilities around the country. The radiologist still does there job, but some odd, random hard to notice conditions, AI is a literal life saver. For example, pancreas divisum, a abnormality in the way the pancreas ducts fail to fuse/etc can cause all kinds of insane issues. But its not something most people know about or look for. AI can pick that up in a second. It can then alert the radiologist of an abnormality and they can then verify. It's enhacing the capabilties of radiologists.
what's the end game here, have a slew of of finetuned models for these varying edgecases?
>Oh yes it is.
>It's enhacing the capabilties of radiologists.
So it is not replacing radiologists?
I guess we have to define 'replace' then. If we need fewer radiologists, does that count as replacing? IDK.
It seems that with AI in particular, many operate with 0/1 thinking in that it can only be useless or take over the world with nothing in between.
Not yet. Time will tell but they have a long way to go if they ever do. They are useful tools now.
Related article from a few weeks ago:
https://www.outofpocket.health/p/why-radiology-ai-didnt-work...
It's interesting to see people fighting so hard to preserve these jobs. Do people want to work that badly? If a magic wand can do everything radiologists can do, would we embrace it or invent reasons to occupy 40+ hours a week of time anyway? If a magic wand, might be on the horizon, shouldn't we all be fighting to find it and even finding ways to tweak our behaviors to maximize the amount of free time that could be generated?
People like the promised stability that comes with certain jobs. Some jobs are sold as "study this, get the GPA, apply to this university and do these things and you will get a stable job at the end". AI plans to disrupt this path.
This isn't going to generate free time. Its going to generate homelessness and increasing wealth inequality.
Our current economic system does not support improved productivity leading to less working (with equal wealth) for the working class.
People enjoy the comfort of consistent food and housing. People also enjoy serving their community. Working helps provide that. So for folks to be willing to sacrifice their security and comfort to get to the horizon of the new day with greater leisure time, it can be scary for many. Especially when you have to make a leap of belief that AI is a magic wand changing your world for the better. Is that supported by the evidence? It's quite the leap in belief and life change. Hesitancy seems appropriate to me.
It is precisely the attraction of the vision that makes people fight so hard to preserve these jobs.
Because we know how well the jobs address a need, and we also know how many times throughout history we have been promised magic wands that never quite showed up.
And guess who is best equipped to measure the actual level of “magic”? Experts like radiologists. We need them the most along the way, not the least.
If a magic wand actually shows up, it will be obvious to everyone and we’ll all adopt it voluntarily. Just like thousands of innovations in history.
Problem: Food, rent, utilities and healthcare cost money.
It's not that so much as no one wants to lose their jobs due to innovation. Just look at typewriter repairmen, TV, radio, even Taxi drivers at one point etc. One day AI and automation will make many jobs redundant, so rather than resisting the march forward of technology, prepare for it and find a way to work alongside, not against innovation.
One thing people aren't talking about is liability.
At the end of the day if the radiologist makes an error the radiologist gets sued.
If AI replaces the radiologist then it is OpenAI or some other AI company that will get sued each and every time the AI model makes a mistake. No AI company wants to be on the hook for that.
So what will happen? Simple. AI will always remain just a tool to assist doctors. But there will always be a disclaimer attached to the output saying that ultimately the radiologist should use his or her judgement. And then the liability would remain with the human not the AI company.
Maybe AI will "replace" radiologists in very poor countries where people may not have had access to radiologists in the first place. In some places in the world it is cheap to get an xray but still can be expensive to pay someone to interpret it. But in the United States the fear of malpractice will mean radiologists never go away.
EDIT: I know the article mentions liability but it mentions it as just one reason among many. My contention is that liability will be the fundamental reason radiologists are never replaced regardless of how good the AI systems get. This applies to other specialities too.
>At the end of the day if the radiologist makes an error the radiologist gets sued.
Are you sure? Who would want to be a radiologist then when a single false negative could bankrupt you? I think it's more likely that as long as they make a best effort at trying to classify correctly then they would be fine.
Doctors have malpractice insurance and other kinds of insurance for that. They won't go bankrupt in reality.
By that logic you would get malpractice insurance for the AI to similarly offload the risk.
Yeah, I mean it's analogous to car insurance for self driving cars. People, including lawyers, insurers and courts are just averse to it intuitively. I'm not saying they are wrong or right, but it's how it is.
I believe medical AI will probably take hold first in a poorer countries where the existing care is too bad/unaffordable, then as it proves itself there, it may slowly find its way to richer countries.
But probably lobbying will be strong against it, just as you can't get cheap generic medications made in India if you live in the US.
While a lot of this rings true, I think the analysis is skewed towards academic radiology. In private practice, everything is optimized for throughput, so the idea that most rads spend less than half of their time reading studies i think is probably way off.
So instead of having to train and employ radiologists, we will train and employ radiologists and pay for the AI inference. Excuse me, but how is this beneficial in any way? It's trivially more expensive, and the result has the same quality? And productivity is also the same?
From the article:
> Some products can reorder radiologist worklists to prioritize critical cases, suggest next steps for care teams, or generate structured draft reports that fit into hospital record systems.
A lot of the tech in the space is workflow related right now. The AI scans triage to the top those cases that are deemed clinically significant, potentially saving time. I can’t tell you it isn’t solely responding to the referring doctor’s urgent stamp though.
Not sure about other hospital systems, but the one I work at is developing CV systems to help fill workforce gaps in places where there isn’t as many trained professionals or even resources to train professionals
>"they struggle to replicate this performance in hospital conditions"
Are there systematic reasons why radiologists in hospitals are inaccurately assessing the AI's output? If the AI models are better than humans in testing novel data then, well, the thing that has changed in a hospital situation compared to the AI-Human testing environment is not the AI, it is the human, under less controlled constraints, additional pressures, workloads, etc. Perhaps the AI's aren't performing as poorly as thought. Perhaps this is why they performed better to begin with. Otherwise, production ML systems are generally not as highly regarded as these models when they perform as significantly below test data sets in production. Some is expected, but "struggle to replicate" implies more.
>"Most tools can only diagnose abnormalities that are common in training data"
Well yes, training on novel examples is one thing. Training on something categorically different is another thing all together. Also there are thresholds of detection. Detecting nothing, or with a a lower confidence, or unknown anomaly, false positive, etc. How much of the inaccuracy isn't wrong, but simply something that is amended or expanded upon when reviewed? Some details here would be useful.
I'm highly skeptical when generalized statements exclude directly relevant information to which an is referring. The few sources provided don't at all cover model accuracy, and the primary factor cited as problematic with AI review, lack of diversity in study composition for women, ethnic variation, children, links to a a meta study that was not at all related to the composition of models and their training data sets.
The article begins as what appears to be a criticism of AI accuracy with the thinness outlined above but then quickly moves on to a "but that's not what radiologists do anyway", and provides a categorical % breakdown of time spent where Personal/Meetings/Meals and some mixture of the others combine to form at least a third that could be categorized as "Time where the human isn't necessary if graphs are being interpreted by models."
I'm not saying there aren't points here, but overall, it simply sounds like the hand-wavy meandering of someone trying to gatekeep a profession whose services could be massively more utilized with more automation, and sure-- perhaps at even higher quality with more radiologists to boot-- but perfect is the enemy of the good etc. on that score, with enormous costs and delays in service in the meantime.
Poorer performance in real hospital settings has more to do with the introduction of new/unexpected/poor quality data (i.e. real world data) that the model was not trained in or optimized for. They still do very well generally, but often do not hit equivalent performance to what is submitted to the FDA, or in marketing materials. This does not mean they aren’t useful.
Clinical AI also has to balance accuracy with workflow efficiency. It may be technically most accurate for a model to report every potential abnormality with associated level of certainty, but this may inundate the radiologist with spurious findings that must be reviewed and rejected, slowing her down without adding clinical value. More data is not always better.
In order for the model to have high enough certainty to get the right balance of sensitivity and specificity to be useful, many many examples are needed for training, and with some rarer entities, that is difficult. It also inherently reduces the value of the model it is only expected to identify its target disease 3 times/year.
That’s not to say advances in AI won’t overcome these problems, just that they haven’t, yet.
For anomaly systems like this, is it effective to invert the problem by not include the ailment/problem in the training data, then looking for a "confused" signal rather than a "x% probability of ailment" type signal?
On that, I'm not sure. My area of ML & data science practice is, thankfully, not so high-stakes. There's a method of anomaly detection called one-class SVM (Support Vector Machine) that is pretty much this- train on normal, flag on "wtf is this you never training me on this 01010##" <-- Not actual ISO standard ML model output or medical jargon. But I'm not sure if that's what's most effective here. My gut instinct in first approaching the task would be to throw a bunch of models at it, mixed-methods, with one-class SVM as a fall back. But I'm also way out of my depth on medical diagnostics ML so that's just a generalist's guess.
I find the radiologist use case an illuminating one for the adoption of AI across business today. My takeaway is that when the tools get better, radiologists aren't replaced, but take up other important tasks that sometimes become second nature when reads (unassisted) are the primary goal.
Reminds me of the "slop" discussions happening right now. When the tools seem good, but aren't, we develop a reliance to false negatives, e.g. text that clearly "feels" written by a GPT model.Building a national remote radiology service would be much more cost effective and accuracy than these unreliable AIs.
I lived this previously. The author is missing some important context.
Spray-and-Pray Algorithms
After AlexNet, dozens of companies rushed into medical imaging. They grabbed whatever data they could find, trained a model, then pushed it through the FDA’s broken clearance process. Most of these products failed in practice because they were junk. In mammography, only 2–3 companies actually built clinically useful products.
Products actually have to be useful.
There were two products in the space: CAD, and Triage. CAD is basically overlay on the screen as you read the case. Rads hated this because it was distracting and because the feature-engineering based CAD from the 80s-90s was demonstrated to be a failure. Users basically ignored "CADs."
Triage is when you prioritize cases (cancers to the top of the stack). This has little to no value because when you have a stack of 50 cases you have to do today, then why do you care about the order? There were some niche use cases but it was largely pointless. It could actually detrimental. The algotithm would put easy cancer cases on the top, so now the user would spend less time on the rest of the stack (where the harder cases would end up).
*Side note:* did you know that using CAD was a billable extra to insurance. Even through it was proven to not work, for years it remained reimbursable up until a few years ago.
Poor Validation Standards
Models collapsed in the real world because the FDA process is designed for drugs/hardware, not adaptive software. Validation typically = ~300 “golden” cases, labeled by 3 radiologists with majority vote arbitration. If 3 rads say it’s cancer, it’s cancer. If they disagree, it's not a good case for the study. This filtering ignores the hard cases (where readers disagree), which is exactly what models need to handle in the real world. Instead of 500K noisy real-world studies, you validate on a sanitized dataset. Companies learned how to “cheat” by over fitting to these toy datasets. You can explain this to regulators endlessly, but the bureaucracy only accepts the previously blessed process. Note: The previous process was defined by CAD, a product that was cleared in the 80s and shown to fail miserably in clinical use. This validation standard that demonstrated grand historical regulatory failure is the current standard that you MUST use for any devices that look like a CAD in mammography.
Politics Over Outcomes
We ran the largest multi-site prospective (15) trial in the space. Results: ~50% reduction in radiologist workload. Increased cancer detection rate. 10x lower cost per study. We even caught cancers missed in the standard workflow. Clinics still resisted adoption—because admitting missed cancers looked bad for their reputation. Bureaucratic EU healthcare systems preferred to avoid the embarrassment even through it was entirely internal.
I'll leave you with one particularly salient story. I was speaking to the head a large US hospital IT/Ops organization. We had a 30 minute conversation about how to avoid putting our software decision in the EMR/PACS so that they could avoid litigation risk. Not once did we ever talk about patient impact. Not Once...
Despite all that, our system caught cancers that would have been missed. Last I checked at least 104 women had their cancers detected by our software and are still walking around. That’s the real win, even if politics buried the broader impact.
Every use of AI has its own problem of "person with 10 fingers" that AI image generation faces and can't seem to solve. For programmers, it's code that calls made up libraries and makes up language semantics. In prose, it's completely incoherent narratives that forget where they are going halfway through. For programmers it's making up case law and citations. Same for scientists, making up authorities and papers and results.
AI art is getting better but still it's very easy for me to quickly distinguish AI result from everything else, because I can visually inspect the artifacts and it's usually not very subtle.
I'm not a radiologist, but I would imagine AI is doing the same thing here, making up things that are cancer, missing things that aren't cancer, and it takes an expert to distinguish the false positives from true. So we're back at square one, except the expertise has shifted from interpreting the image to interpreting the image and also interpreting the AI.
> AI art is getting better but still it's very easy for me to quickly distinguish AI result from everything else, because I can visually inspect the artifacts and it's usually not very subtle.
I actually disagree in that it's not easy for me at all to quickly distinguish AI images from everything else. But I think we might differ what we mean by "quickly". I can quickly distinguish AI if I am looking. But if I'm mindlessly doomscrolling I cannot always distinguish 'random art of an attractive busty woman in generic fantasy armor that a streamer I follow shared' as AI. I cannot always distinguish 'reply-guy profile picture that's like a couple dozen pixels in dimensions' as AI. I also cannot always tell if someone is using a filter if I'm looking for maybe 5 seconds tops while I scroll.
AI art is easy to pick out when no effort was made to deviate from the default style that the models use. Where the person put in a basic prompt of the desired contents ("man freezing on a bed") and calls it a day. When some craftsmanship is applied to make it more original, that's when it gets progressively harder to catch it at first glance. Though I'd argue that it's more transformative and thus warrants less criticism than the lazy usage.
As a related aside, I've started seeing businesses clearly using ChatGPT for their logos. You can tell from the style and how much random detail there is contrasted with the fact that it's a small boba tea shop with two employees. I am still trying to organize my thoughts on that one.
Edit:
Example: https://cloudfront-us-east-1.images.arcpublishing.com/brookf...
The thing with medical services is that there is never enough.
If you are rich and care about your health (especially as move past age 40), you probably have use for a physiotherapist, a nutritionist, therapist, regular blood analysis, comprehensive cardio screening, comprehensive cancer screening etc. Arguably, there is no limit to the amount of medical services that people could use if they were cheap and accessible enough.
Even if AI tools add 1-2% on the diagnostic side every year, it will take a very, very long time to catch up to demand.
The only part of this article I believe is the legal and bureaucratic burdens part.
"Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians"
I've had the misfortune of dealing with a radiologist or two this year. They spent 10-20 minutes talking about the imaging and the results with me. What they said was very superficial and they didn't have answers to several of the questions I asked.
I went over the images and pathology reports with ChatGPT and it was much better informed, did have answers for my questions, and had additional questions I should have been asking. I've used ChatGPT's information on the rare occasions when doctors deign to speak with me and it's always been right. Me, repeating conclusions and observations ChatGPT made, to my doctors, has twice changed the course of my treatment this year, and the doctors have never said anything I've learned from ChatGPT is wrong. By contrast, my doctors are often wrong, forgetful, or mistaken. I trust ChatGPT way more than them.
Good image recognition models probably are much better than human radiologists already and certainly could be vastly better. One obstacle this post mentions - AI models "struggle to replicate this performance in hospital conditions", is purely a choice. If HMOs trained models on real data then this would no longer be the case, if it is now, which I doubt.
I think it's pretty clearly doctors, and their various bureaucratic and legal allies, defending their legal monopoly so they can provide worse and slower healthcare at higher prices, so they continue to make money, at the small cost of the sick getting worse and dying.
Can radiology results be instantly fed into pass/fail metrics like code can via tests?
Programming is the first job AI will replace. The rest come later.
As the prediction of radiologists going dodo sprung out from improvements in image recognition, why don't we see premature and hysterically hyped predictions of psychiatrists being unemployed due to language models.
Their workday consists of conversations, questions and reading. Something LLMs more than excel at doing, tirelessly and in huge volumes.
And if radiologists are still the top bet due to image recognition being so much hotter, then why not add dermatologists to the extinction roster? They only ever look at regular light images, it should be a lower hanging fruit.
(I'm aware of the nuances that make automation of these work roles hard, I'm just trying to shine some light on the mystery of radiologists being perceived as the perennial easy target)
So... is it technically possible?
AI _is_ replacing radiologists. Where AI stands for "An Indian". Search for teleradiology, on Google and on HN too.
You do realize that in order to interpret imaging for a US based patient, any physician needs to have a US medical license?
Any physician with a medical license in the US can sign off on an Indian physician's work. Or hundreds of them.
I personally think we are in the pre-broadband era of AI. That is, what is currently being built will contribute to some AI dot-com 1.0 bubble burst, but after that there will be some advancements in it's distribution model that will allow it to thrive and infiltrate our society at a core level. Right now it's a bunch of dreamers building the pets.com that no one asked for, but after all the competition is shaken off, there will definitely be some clear winners, and maybe an Amazon.com of sorts for AI that people will adopt.