6 ms·
Radiology-specific foundation model
- nightski 2y agoIs it really a foundation model if it is for a specific purpose?
- marsh_mellow 2y agoThey list seven different use cases in this technical blog: https://harrison.ai/news/reimagining-medical-ai-with-the-most-powerful-large-multimodal-foundational-model-designed-to-excel-in-radiology/ https://harrison.ai/news/reimagining-medical-ai-with-the-mos... I'd interpret it as a foundation model in the radiology domain
- deleted 2y ago[deleted]
- smitec 2y agoA very exciting release and I hope it stacks up in the field. I ran into their team a few times in a previous role and they were always extremely robust in their clinical validation which is often lacking in the space. I still see somewhat of a product gap in this whole area when selling into clinics but that can likely be solved with time.
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- newyankee 2y agoI wonder if there is any open source radiology model that can be used to test and assist real world radiologists
- amitport 2y agothere are a few for specific tasks (e.g., lung cancer), no "foundation" models afaikt.
- zxexz 2y agoThere really should be at this point. Annotated radiology datasets, patients numbering into the millions, are the easiest healthcare datasets to obtain. I suspect there are many startups, and know of several long since failed, who trained on these. I've met radiologists who assert most of their job comes down to contextualizing their findings to their colleagues, as well as within the scope of the case itself. That's relevant here - it doesn't matter how accurate or precise your model is, if it can't do that. Radiologists already use "AI" tools that are very good, and radiology is a very welcoming field for new technology. I think the promise of foundation models at the moment would be to ease burden and help prevent burnout. Unfortunately, those models aren't "sexy" - they reduce administrative burden, assemble contextual evidence for better retrieval (have interfaces that don't suck when integrated with the EMR).
- zxexz 2y agoI recall there being a couple non-commercial ones on physionet trained on the MIMIC CXR dataset. I could be wrong, I'll hopefully remember to check.
- infocollector 2y agoI don't see a release? Perhaps its an internal distribution to subscribers/people? Does anyone see a download/github page for the model?
- blazerunner 2y agoI can see a link to join a waitlist for the model, as well there is this: > Filtered for plain radiographs, Harrison.rad.1 achieves 82% accuracy on closed questions, outperforming other generalist and specialist LLM models available to date (Table 1). The code and methodology used to reach this conclusion will be made available at https://harrison-ai.github.io/radbench/ https://harrison-ai.github.io/radbench/.
- stevenbuscemi 2y agoHarrison.ai typically productionize and commercialize their models through child companies (Annalise.ai for radiology, Franklin.ai for pathology). I'd imagine access to the model itself will remain pretty exclusive, but would love to see them adopt a more open approach.
- _p2zi 2y agoI can't find any git link, hopefully I will look into it later. From their benchmarks it's looking like a great model that beat competition, but I will see the third party tests after they get released to determine the real performance.
- nopinsight 2y agoThis is impressive. The next step is to see how well it generalizes outside of such tests. "The Fellowship of the Royal College of Radiologists (FRCR) 2B Rapids exam is considered one of the leading and toughest certifications for radiologists. Only 40-59% of human radiologists pass on their first attempt. Radiologists who re-attempt the exam within a year of passing score an average of 50.88 out of 60 (84.8%). Harrison.rad.1 scored 51.4 out of 60 (85.67%). Other competing models, including OpenAI’s GPT-4o, Microsoft’s LLaVA-Med, Anthropic’s Claude 3.5 Sonnet and Google’s Gemini 1.5 Pro, mostly scored below 30*, which is statistically no better than random guessing."
- rafram 2y agoImpressive, but was it trained on questions from the exam? Were any of those other models?
- aengustran 2y agoharrison.rad.1 was not trained on any of the exam questions. It can't be guaranteed however that other models were not trained on them though.
- isaacfrond 2y agoFrom the article: Other competing models, including OpenAI’s GPT-4o, Microsoft’s LLaVA-Med, Anthropic’s Claude 3.5 Sonnet and Google’s Gemini 1.5 Pro, mostly scored below 30*, which is statistically no better than random guessing. How is chatgpt the competion? It’s mostly a text model?
- seanvelasco 2y agofollowing this, gonna integrate this with a DICOM viewer i'm developing from the ground up
- lostlogin 2y agoFixing the RIS would make radiology happier than fixing the viewer. And while you’re at it, the current ‘integrations’ between RIS and PACS are so jarring it sets my teeth on edge.
- rasmus1610 2y agoYes please. We hope to move away from our RIS and integrate our reporting workflow into our PACS this year
- rahkiin 2y agoCan you help me with those acronyms?
- squigz 2y ago'Radiology Information System' and 'Picture Archive and Communication System', I think https://www.adsc.com/blog/what-are-the-differences-between-pacs-ris-cis-and-dicom https://www.adsc.com/blog/what-are-the-differences-between-p...
- tecleandor 2y agoThat's correct!
- tecleandor 2y agoRadiology Information System. The software that manages the radiology workflow in a clinic. Schedules radiology studies, exchanges information with the modalities (the radiology devices) so the studies have proper metadata, exchanges information with the PACS (the radiology image storage), it might be used by radiologists and/or transcriptionists to add the reports for the studies... It might overlap a bit with the HIS (hospital information system) that's the more general hospital management software.
- nradov 2y agoI'm glad to see that this model uses multiple patient chart data elements beyond just images. Some earlier more naive models attempted to treat it as a pure image classification problem which isn't sufficient outside the simplest cases. Human radiologists rely heavily on other factors including patient age, sex, previous diagnoses, patient reported symptoms, etc.
- lostlogin 2y ago> patient reported symptoms You make it sound like the reporting radiologist is given a referral with helpful, legible information on it. That this ever happened doubtful.
- nradov 2y agoReferrals are more problematic but if the radiologist works in the same organization as the ordering physician then they should have access to the full patient chart in the EHR.
- owenpalmer 2y agoI had an MRI on my ankle several years ago. At first glance, the doctor told me there was nothing wrong, even though I had very painful symptoms. While the visit was unproductive, I requested the MRI images on a CD, just because I was curious (I wanted to reconstruct the layers into a 3D model). After receiving the data in the mail weeks later, I was surprised to find a formal diagnosis on the CD. Apparently a better doctor had gotten around to analyzing it (they never followed up). If I hadn't requested my records, I never would have gotten a diagnosis. I had a swollen retrocalcaneal bursa. I googled the treatments, and eventually got better. I'm curious whether this AI model would have been able to detect my issue more competently than the shitty doctor.
- lostlogin 2y agoHow did this happen? Surely your results went to a requesting physician who should have been following up with you? Radiology doctors don’t usually organise follow up care. Or was the inaccurate result from the requesting physician?
- owenpalmer 2y agoI don't know, just incompetence and disorganization on their part. Directly after my MRI, they told me the images didn't indicate any meaningful information.
- quantumwoke 2y agoThe radiographer or the radiologist? Did you see your requesting doctor afterwards?
- rscho 2y agoYou got lost in the mess of files and admin. The process is usually that you get the exam, they give you a first impression orally. Then they really get to work and look properly, and produce a written report, which the requesting doc will use for treatment decisions. At that point, they're supposed to get back to you, but apparently someone dropped you along the way.
- davedx 2y ago“AI has peaked” “AI is a bubble” We’re still scratching the surface of what’s possible. I’m hugely optimistic about the future, in a way I never was in other hype/tech cycles.
- Almondsetat 2y ago"AI" here refers to general intelligence. A highly specific ML model for radiology is not AI, but a new avenue for improvements in the field of computer vision.
- the8472 2y agoSo, hypothetically, a general-intelligence-capable architecture isn't allowed to specialize in a particular task without losing its GI status? I.e. trained radiologists wouldn't be a general intelligence? E.g. their ability to produce text is really just a part of their radiologist-function to output data, right?
- Almondsetat 2y agoIt's impossible for humans to know a lot about everything, while LLMs can. So an LLM that sacrifices all that knowledge for a specific application is no longer an AI, since it would show its shortcomings more obviously.
- whamlastxmas 2y agoThe world’s shittiest calculator powered by a coin battery is an AI. I think you’re being overly narrow or confusing it with AGI
- deleted 2y ago[deleted]
- the8472 2y agoThey're still very bounded systems (not some galaxy brain) and training them is expensive. Learning tradeoffs have to be made. The tradeoffs are just different than in humans. Note that they're still able to interact via natural language!
- ilaksh 2y agoI think the only real reason the general public can't access this now is greed and a lack of understanding of technology. They will say that it is dangerous or something to let the general public access it because they may attempt to self-diagnose or something. But radiologists are very busy and this could help many people. Put a strong disclaimer in there. Open it up to subscriptions to everyone. Charge $40 per analysis or something. Integrate some kind of directory or referral service for human medical professionals. Anyway, I hope some non-profit organizations will see the capabilities of this model and work together to create an open dataset. That might involve recruiting volunteers to sign up before they have injuries. Or maybe just recruiting different medical providers that get waivers and give discounts on the spot. Won't be easy. But will be worth it.
- ImHereToVote 2y agoDoctors should be like thesis advisors for their patients. If the patients undergo a minimum competency test. If you can't pass. You don't get a thesis advisor.
- arathis 2y agoYou think the only real reason the public don't get to use this tool is because of greed? Like, that's the only REAL reason? Not the technological or ethical implications? The dangers in providing people with no real concept of how any of this works the means to evaluate themselves?
- K0balt 2y agoYeah, we should also limit access to medical books too. With a copy of the MERK manual, what’s to stop me from diagnosing my own diseases or even setting up shop at the mall as a medical “counselor” ? The infantilization of the public in the name of “safety” is offensive and ridiculous. In many countries, you can get the vast majority of medicines at the pharmacy without a prescription. Amazingly, people still pay doctors and don’t just take random medications without consulting medical professionals. It’s only “necessary” to limit access to medical tools in countries that have perverted the incentive structure of healthcare to the point where, out of desperation, people will try nearly anything to deal with health issues that they desperately need care for but cannot afford. In countries where healthcare costs are not punitive and are in alignment with the economy, people opt for sane solutions and quality advice because they want to get well and don’t want to harm themselves accidentally. If developing nations with arguably inferior education systems can responsibly live with open access to medical treatment resources like diagnostic imaging and pharmaceuticals, maybe we should be asking ourselves what is it, exactly, that is perverting the incentives so badly that having ungated access to these lifesaving resources would be dangerous?
- joelthelion 2y agoToo bad it's not available llama-style. We'd see a lot of progress and new applications if something like that was available.
- daedalus_f 2y agoThe FRCR 2b examination consists of three parts, a rapid reporting component (the candidate assess around 35 x-rays in 30 minutes where the candidate is simply expected to mark the film as normal or abnormal, this is a perceptual test and is largely limited to simple fracture vs normal) alongside a viva and long cases component where the candidate reviews more complex examinations and is expected to provide a report, differential diagnosis and management plan. A quick look at the paper in the BMJ shows that the model did not sit the FRCR 2b examination as claimed, but was given a cut down mock up of the rapid reporting part of the examination invented by one of the authors. https://www.bmj.com/content/bmj/379/bmj-2022-072826.full.pdf https://www.bmj.com/content/bmj/379/bmj-2022-072826.full.pdf
- nopinsight 2y agoThe paper you linked to was published in 2022. The results there were for a different system for sure. Were the same tests also used here?
- jarrelscy 2y agoOne of the developers here. The paper links to an earlier model from a different group that could only interpret X-rays of specific body parts. Our model does not have such limitation. However, the actual FRCR 2B Rapids exam question bank is not publicly available and the FRCR is unlikely to agree to release them as this would compromise the integrity of their examination in the future- so the test used are mock examinations, none of which have been provided to the model during training.
- daedalus_f 2y agoInteresting, is your model still based on radiographs alone, or can it look at cross-sectional imaging as well?
- jarrelscy 2y agoThis current model is radiographs alone. The FRCR 2B Rapids exam is based on only radiographs.
- ethanmitchell87 2y ago[dead]
- ZahiF 2y agoSuper cool, love to see it. I recently joined [Sonio](https://sonio.ai/platform/ https://sonio.ai/platform/), where we work on AI-powered prenatal ultrasound reporting and image management. Arguably, prenatal ultrasounds are some of the more challenging to get right, but we've already deployed our solution in clinics across the US and Europe. Exciting times indeed!
- whamlastxmas 2y agoIt’s weird that I have to attest I’m a healthcare professional just to view your job openings
- haldujai 2y ago> Arguably, prenatal ultrasounds are some of the more challenging to get right Prenatal ultrasounds are one of the most rote and straight forward exams to get right.
- trashtester 2y agoAI models for regular X-rays seems to be achieving high quality human level performance, which is not unexpected. But if someone is able to connect a network to the raw data outputs from CT or MR machines, one may start seeing these AI's radically outperform humans at a fraction of the cost. For CT machines, this could also be used to concentrate radiation doses into parts of the body where the uncertainty of the current state is greatest, even in real time. For instance, if using a CT machine to examine a fracture in a leg bone, one could start out with a very low dosage scan, simply to find the exact location of the bone. Then slightly higher concentrated scan of the bone in the general area, and then an even higher dosage in an area where the fracture is detected, to get a high resolution picture of the damage, and splinters etc. This could reduce the total dosage the patient is exposed to, or be used to get a higher resolution image of the damaged area than one would otherwise want to collect, or possibly to perform more scans during treatment than is currently considered worth the radiation exposure. Such machines could also be made multi modal, meaning the same machine could carry both CT, MR, ultrasound sensors (dopler + regular). Possibly even secondary sensors, such as thermal sensors, pressure sensors or even invasive types of sensors. By fusing all such inputs (+ the medical records, blood sample data etc) for the patient, such a machine may be able to build a more complete picture of a patient's conditions than even the best hospitals can provide today, and a at a fraction of the cost. Especially for diffuse issues, like back pains where information about bone damage, bloodflow (from the Doppler ultrasound), soft tissue tension/condition etc could be collected simultaneously and matched with the reported symptoms in real time to find location where nerve damage or irritation could occur. To verify findings (or to exclude such, if more than one possible explanation exists), such an AI could then suggest experiments that would confirm or exclude possibilities, including stimulating certain areas electrically, apply physical pressure or even by inserting some tiny probe to inspect the location directly. Unfortunately (or fortunately to the medical companies), while this cold lower the cost per treatment, the market for such diagnostics could grow even faster, meaning medical costs (insurance/taxes) might still go up with this.
- deleted 2y ago[deleted]
- naveen99 2y agoXray specific model. fractures are relatively easy. Chest and abdomen xrays are hard. Very large chest xray datasets have been out for a long time (like from stanford). problem solving is done with ct, ultrasound, pet, mri, fluoroscopy, other nuclear scans.
- hammock 2y agoI looked at my rib images for days trying to find the fracture. Couldn't do it. Could barely count the ribs. All my doctor friends found it right away though
- naveen99 2y agoOk, yeah rib fractures on chest X-rays are hard also. Even extremity Fractures can be hard also. Some are not directly visible, but you can look for indirect signs such as hematomas displacing fat pads. Stress fractures show up only on mri or bone scans…
- augustinemp 2y agoI spoke to radiologist in a customer interview yesterday. They mentioned that they would really like a tool that could zoom on a specific part of an image and explain what is happening. For extra points they would like it to be able to reference literature where similar images were shown.
- Workaccount2 2y agoAren't radiologists that "tool" from the perspective of primary doctors?
- darby_nine 2y agoSort of like primary doctors are just a "tool" to get referrals for treatment
- hgh 2y agoConnecting your comment to another about commercial model, seems the potential win here is selling useful tools to radiologists that may leverage AI rather than to end customers with the idea to replace some radiology consultations. This seems generally aligned with AI realities today: it won't necessarily replace whole job functions but it can increase productivity when applied thoughtfully.
- aqme28 2y agoThis is far from the first company to try to tackle AI radiology, or even AI x-ray radiology. It's not even the first company to have a model that works on par or better than radiologists. I'm curious how they solve the commercial angle here, which seems to be the big point of failure.
- crabbone 2y agoThe real problem is liability. Radiologist, if they make a mistake can be sued. Who are you going to sue when the program misdiagnoses you? NB. In all claims I've seen so far about outperforming radiologist, the common denominator was that people creating these models have mostly never even seen a real radiologist and had no idea how to read the images. Subsequently, the models "worked" due to some kind of luck, where they accidentally (or deliberately) were fed data that made them look good.
- moralestapia 2y ago"Exclusive Dataset" "We have proprietary access to extensive medical imaging data that is representative and diverse, enabling superior model training and accuracy. " Oh, I'd love to see the loicenses on that, :^).
- husarcik 2y agoAs a radiology resident, it would be nice to have a tool to better organize my dictation automatically. I don't want to ever have to touch a powerscribe template again. I'd be 2x as productive if I could just speak and it auto filled my template in the correct spots.
- transcranial 2y agoAs a former radiology resident, I totally agree. That's why we're building exactly this: https://md.ai/product/reporting/ https://md.ai/product/reporting/.
- akashtomer1 2y ago[dead]
- hammock 2y agoRadiology is the best job ever. Work from home, click through pictures all day. Profit
- bobbiechen 2y ago"We'd better hope we can actually replace radiologists with AI, because medical students are no longer choosing to specialize in it." - one of the speakers at a recent health+AI event I'm wondering what others in healthcare think of this. I've been skeptical about the death of software engineering as a profession (just as spreadsheets increased the number of accountants), but neither of those jobs requires going to medical school for several years.
- yurimo 2y agoInteresting take. Had a friend recently start medschool (in US) and he said radiology was some of the top directions people were considerings, because as he put it "the pay is decent and they have a life". Anecdotal but I wonder what is the reason of not specializing in it then. If anything AI can help reduce the workload further and identify patterns that can be missed.
- husarcik 2y agoI'm a third year radiology resident. That speaker is misinformed as diagnostic radiology has become one of the most competitive subspecialties to get into. All spots fill every year. We need more radiology spots to keep up with the demand.
- doctoring 2y agoI don't know for other countries, but for the United States, "medical students are no longer choosing it" is very very untrue, and it is trivial to look up as this information is public from the NRMP (the organization that runs the residency match). Radiology remains one of the most competitive and in-demand specialties. In this year's match, only 4 out of ~1200 available radiology residency positions went unfilled. Last year was 0. Only a handful of other specialties have similar rates. As comparison, 251 out of ~900 pediatric residency slots went unfilled this year. And 636 out of ~5000 family medicine residency slots went unfilled. (These are much higher than previous years.) However, I do somewhat agree with the speaker's sentiment if for a different reason. Radiologist supply in the US is roughly stable (thanks to the US's strange stranglehold on residency slots), but demand is increasing: the number of scans ordered on a per patient continues to rise, as does the complexity of those scans. I've heard of hospital systems with backlogs that result in patients waiting months for, say, their cancer staging scan. One can hope we find some way to make things more efficient. Maybe AI can help.
- Achara 2y ago[flagged]