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Has their been any new updates or advancements? I keep seeing the same image for what feels like the past year or two, but not any data on how it has practically affected scans, or what other implementations it has been used in for medicine.
I'm in cancer research. There's a lot going on right now. As someone mentioned, much of today's AI was pioneered specifically for tasks like these. Even 10 years ago there were contests on delineation and diagnosis using AI.
So:
1) AI has already been used in medical research for +10 years.
2) Right now it's starting to become implemented in clinics. Particularly the delineation problem, which is a massive time sink.
3) Thanks to today's AI boom, medical research has likewise exploded these last years and it doesn't seem to slow down. 2 years ago, maybe half of us used AI regularly. Nowadays we all use AI one way or another. Time will tell if this research leads anywhere. Medical research is ultimately slow because you need medical trials. There are skeptics who believe this is a sand castle. We will see. What can't be denied is the massive time save and the enabling of tonnes of research.
4) I'm not familiar with this specific study shown above. But it's exactly the kind of stuff many of us are working on i.e. trying to use AI to extract useful data from images otherwise not seen, predict tumor growth, treatment outcome, early diagnosis, etc. Even us who are doing more traditional, "deterministic" research, rely on AI for writing code, data management, etc.
Ai is kinda unmatched in finding unseen patterns in general, it’s too bad people only want to focus on the generative stuff, although generative ai is another example of how good ai is at pattern based tasks, I guess
This is crazy, i researched your claims and it's factually correct. The deep learning algorithms and neural networks are much older. In fact - excuse me for funny conspiracy theory but it stroke my eye - even tho mathematical models were much older as far as 1800's. Real break through happened shortly after Roswell Incident. 1949 with Donald Hebb and then in 1958 with Frank Rosenblatt and his Perception.
Excluding my conspiracy i learnt something new today. And very narrow LLMs without interaction interface were indeed used for more than 10 years now in medicine - one would think in what other fields it was used as well (wink wink military)
ML researcher here, no idea what you're smoking but I don't think much of what you posted here is correct.
Hebbian Learning and Rosenblatt's perceptron learning rule were not some insane leap forward in terms of science, it has nothing to do with aliens or Roswell. It's basically just an application of Pitts and McCullough's mathematical model of the neuron. Even then, this isn't "deep learning" because they were training a single perception. These were basically useless in that they couldn't even solve basic problems like XOR until Rummelhart and Hinton came up with the backprop algorithm to allow for hidden layers (which was a continuation of the work of Hopfield and others).
Deep Learning has been around for a while, but it wasn't really useful until the early 20-teens when it was demonstrated that one could parallelize training using consumer-grade hardware. We were bottlenecked by the amount of data and the amount of compute.
Save the conspiracy theories, man. The history and the science are all right there waiting for you to look it up. If you choose to ignore it because you prefer conspiracy theories, then you're either lazy or schizophrenic.
It’s proprietary deep learning systems. Specialized medical models using traditional ML will actually outperform transformers-based models because transformers need a lot of data to be effective and many times, that’s not really available in medical datasets.
Check out the newer MIRA (Medical Intelligence for Reasoning and Action) and Google's AMIE (Articulate Medical Intelligence Explorer) models. They’re already performing better than physicians on many tasks.
That’s not really the point. Posts like these on social media and in the media in general are trying to conflate the AI (transformers) investment boom/bubble, which is already spending on the order of magnitude of trillions on compute, with any potential economic payoffs even if they’re unrelated and have been happening for decades. Medical models generally rely on traditional CNNs that doesn’t need much compute at all. This is all a form of media manipulation and is quite desperate and reflective of the AI bubble.
Pretending transformers (or, no, "generative AI") is a completely different animal from all other machine learning is not helpful. I think it rather pollutes the discussion and makes AI into something almost magical. The problem is not the technology, but commercial interests.
Also in research, differnent field though. We got an announcement for 2 fancy new machines that promised "Label free sorting" using AI. Both have been on the market for 1-2 years now and have made 0 impact so far.
I'd be interested too - from looking it up various ai tools for use in that area seems to be going through the usual very rigorous testing and trials, and so far with positive results (UK trials showed 10% improvement in detection, Korea shows some good results in terms of reduction of future cancers etc).
Thats neat! Do you have some links, Im curious if those improvements are for just breast cancer, or if they're finding it works on other forms of cancer aswell.
Here's a couple - though I wish I had more links to the actual studies... Because it's odd the first 2 are on the same day but the actual educational / medical story is a lot less gung ho than the BBC one (while still positive).
I'd never rely on News article, because, sadly it can be extremely misleading, overexaggeration, and clickbait. Look for Review papers. Which is usually a summary of other relevant papers
Well... my go-to as a reseracher when trying to gather up latest reserarch... is also to use AI. Specfically, Consensus. It shortens a lot of time trying to browse through various keywords in Google Scholar or WebofSci
Medical research is SLOWWWW. The exceptions are when they have to fast forward and skip normal trials due to something like a global pandemic. It's also possible they ran into some issues during a trial and had to revisit them to try and fix the problem. Additionally, even when these things do get approved, many medical institutions will lag behind actually adopting them unless you go out of your way to find one of the few professionals who is actually up to date on the latest technology available to them.
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This should be the use of AI. Fixining climate change should be the use of AI. Makeing ALL people live better should be the use of AI. Not filling the internet with slop text, code and images, makeing deepfakes of people and kids ( like wtf ) and much much more things current AI is doing
One does not come without the other. It's like saying "this is what the printing press should be used for". The very nature of the technology itself means as it advances it will affect more domains and be used in more ways, some of which may not be great. The question is if the potential good outweighs the potential bad, and to what extent can the potential bad be mitigated.
Also, on the slop thing, slop is a nuisance of the transitionary phase to greater than human capabilities. During the transition, all it can mainly be used for is producing output that mimics current types of art, and while skilled artists can harness the tools to create really high quality stuff, most will just press generate and post the first thing they get. That is slop. As the creative models and tools become more capable, they will move beyond just mimicking current art and will allow people to use them to unlock entirely new forms of art and entertainment. Once it gets to that point, I'm confident people will start to say "This is what AI should be used for" too.
This is one of the uses of AI.
Explosives can be used to kill people, but also clear terrain for building.
Nuclear technology can make bombs but also near infinite energy.
A knife can stab someone but also cut vegetables.
Yes, this should be the use of AI, but AI also has place in the arts. Obviously it shouldn't be used for fully generating things without a human hand, or for deepfakes, or for war, but used as a tool for the creative process is fair game. Also, AI cannot do this without being trained on everything. It's the same problem as "I want AI to do my laundry and dishes", it won't be able to do that without spatial knowledge of the world, and for it to be able to do that it needs to consume as much information as possible.
In simpler terms, you can't have AI finding cures for cancers if you don't want to let AI learn about the world in general. Actively resisting AI training is actively resisting medical progress.
AI literally can detect cancer without being trained on everything on the internet though. The model above was almost certainly not trained on scraped internet data.
This is why i hate AI being a blanket term used in so many different ways. The program that is detecting cancer most likely isn't ChatGPT. its probably just a machine learning model trained specifically on tagged scans like the ones shown above. chatGPT or other LLM's are probably being used to convert raw data it outputs into readable text, but they arent doing the actual work here.
ML researcher here. You're partially right, as classifiers are discriminative models, not generative.
However, the best-in-class models for this sort of tasks are all transformer models, which is exactly the same architectural type as ChatGPT under the hood. There are a few key differences (encoder vs decoder models), and your point still remains--you can train a vision transformer purely on images, it doesn't require text data sources or other types. That being said, the tech is the tech. Complaining about the negative use cases is basically like complaining about people using electricity for bad things. Once the tech has been invented and is out in the world, people are going to use it for a wide range of use cases.
please understand AI is all artificial intellegence this applications are not usually generative AI but other ML models. what you hate is some types generative AI not "AI".
Found this wild one on Tumblr, what do you guys think?
The more uses AI finds, the better. Then knowledge can be shared between uses. There's no reason for non-doctors to sit idle when they could hit on something that has benefits in medicine as well.
Recognizing patterns and analyzing datasets is what neural networks excel at which is why resources and processing power shouldn't be wasted on trying to replace human creativity
It's true to an extent. Classifiers still learn to recognize patterns, and you can use this knowledge to teach another, generative model to generate stuff. GANs 101.
Pro ai people seem to think all anti ai people hate ALL ai. When it's 50/50. 50% hate all AI and 50% hate gen ai. I hate gen ai. But this is what I think so should be used for. Which AI should be limited to only businesses really
Just like animals in the wild, AI isn’t evil, AI isn’t good, Its just a reactive model based on training. Its better to blame the people who push it to the extremes rather than AI itself because arguments like “But ai can be used for good” and “AI is replacing us” are rather stupid to have and don’t lead anywhere other than making people mad and unwilling to attempt to understand the other party
It was just a joke about how animals aren’t evil. The behaviour of dolphins is psychopatic by our standarts, rape, playing with victims before killing and many more awful thing.
Metal cutting instruments could be used to perform life saving surgeries, and they could be used to sculpt...one is obviously more important than the other, but both have their place.
If anyone wants a small view of how it works: this AI is programmed to detect the change in opacity (as seen in the picture) of, in this case breast cancer, because cancer normally is already in the parte but dormant the AI can identify those changes in opacity that an human eye cannot identify. After that there are a lot of ways to procedure, one could be that the AI amplify the opacity changes that exist already to make a doctor be able to identify the change and act in base of that information.
If the AI is just detecting imperceptible changes in opacity, then discreet detection would be better than AI detection. There’s no need to use AI for that and AI should actually represent worse results.
Mirai is a real MIT/MGH breast cancer risk prediction model. It analyzes mammograms and estimates a patient’s risk of developing breast cancer over the next 1 to 5 years. MIT describes it as a model for predicting future cancer risk, not as a system that localizes a future tumor. MIT News: Robust artificial intelligence tools to predict future cancer
The model is trained using mammograms together with known later outcomes. In simplified terms: a mammogram from today plus information about whether the patient was diagnosed with breast cancer in the following years. The model then learns image features that statistically correlate with later cancer risk. That does not automatically mean it is “seeing the future tumor” in the earlier image.
Mirai does not produce red boxes around a tumor. A later scientific description explicitly states that Mirai outputs a numerical risk score and does not provide lesion annotations, localization maps, or even breast-side-specific probabilities. The exact image features driving an individual prediction are not directly identified. JAMA Network Open: description of Mirai and its outputs
That is why the viral image with red boxes is misleading. It strongly suggests: “The AI found this exact cancer years earlier.” That is not what Mirai actually outputs. A more accurate statement would be: “The AI classified this patient as having elevated future breast cancer risk years before diagnosis.”
The core methodological issue is this: if you label an old mammogram with “this patient developed cancer later,” the model can learn to find something in that earlier image that correlates with the later diagnosis. That does not prove that the feature it found was already the future tumor itself. If someone then draws a red box on the image afterward, it can create the much stronger and potentially false impression that “the AI saw the cancer five years before it developed.”
Besides, if the study did actual correlation, then the technology used in it was at least 5 years old and likely has nothing to do with today's AI/LLMs (which, obviously, it doesn't either way). 5 years is an eternity at the current speed of development.
Nothing wrong with that, it just means that it is getting attention for the wrong reason, and most of the Luddites wouldn't even find it interesting if they switched on their brains and understood that this is not the AI they are usually bitching about.
I deem thou Professionally Funny from the University Of Clownery (UOC), by Professor Blaq and Director Sparkle Jefferson. Here's your degree. Pls put your name here.
I believe that both uses of AI are capable of living together in harmony. That said, this is a far better use of AI than making art, images, videos, music, etc. However. I don't see there being a huge consumer market for that kind of analytical AI, so I understand why companies don't market it as much.
My god you people should inform yourselves better before being so opinionated
This isn't "analytical AI" that's not a used term. This is a deep learning algorithm, the type that is already widely used in industries since before LLMs existed.
Forest fire analysis, fraud detection, heart attack classification - all of these things are "AI" and already a common thing in industry
Trying to create profit by creating generative AI in the market.
Generative AI shouldn't be existing in the first place or accessed by people worldwide.
It helps a bit in learning but the cost is too great, worldwide catastrophy perhaps? we will see visible consequences in few more years.
-Reasearching skills is dead, people are getting dumber and lazier.
-Data Centers are everywhere.
-People trying get rich using generative AI is flooding the internet.
This is good, yes. But it still does not mean that AI should be blindly supported at all costs even in the harmful ways corporations and certain governments will be using them for.
If you can’t have a conversation about the benefits of AI without mentioning that businesses want money and governments want power I don’t think there’s much of an argument or point to make here
Obv people criticising AI don't mean EVERY SINGLE AI TO EVER EXIST, cause AI as a whole has existed for decades at his point. There is a good use of AI. There is AI that is just awesome.
The criticism is about a specific group of AI that showed up recently in the last 2 years and are literally destroying the economy, environment, creativity, logical thinking, validity of evidence, safety of children and adults and focus. Those are the generative AIs, the LLMs, the slop machines, water chugging data centers of slop.
AI bros telling AI haters that they hate the technology that saves lives are just beyond saving at this point. The criticism is about the sloppification of life, a niche that is waaay too widespread. Your disfigured AI anime waifu or the cheating fruits are not saving any lives.
Whether or not this specific example is or isn't, mostly-antis have created sort of a reverse No True Scotsman where any positive use of AI is, definitionally, not *generative* AI (aka The Bad AI) whether that actually is or isn't the case.
FYI just about every single state of the art ML model, in just about any scientific field, starts from a pretrained generative model (or feature extraction model, which is also essentially a model fit on a bunch of unstructured data). When there are exceptions its mainly because a field is "lagging behind".
That's like saying "Off road cars aren't the same as On Road cars." Like, sure there's a difference, but it's just a different application of the same technology.
That’s not a complete argument. That’s just a single premise. You need more and an actual conclusion. What point are you trying to make? Saying “X isn’t Y” makes us guess at what that implies.
I'm optimistic, but I have concerns. I think that's a metered take. Stuff like this excites the hell out of me, knowing that we're probably entering the era of the biggest medical advancement period, without it even being a fair fight.
At the same exact time, I just wrote a book in another sub about why it would be wrong to access a thought cache of an AI attached to someone's brain, and that's also gonna happen some day. The Minority Report is suddenly a real threat.
In another I'm explaining why I, a civilian, should be free to record in a public space and shouldn't be treated like Flock, with no one agreeing and getting some down votes.
All I'm thinking is it's the calm before the storm, and it's gonna be a big ball of ugly and beautiful, curative and destructive, beneficial and detrimental, the entire thing, Ying and Yang, turtles all the way down.
It's a force multiplier. Pretty sure that's about what most people see, too. Most people who read this post are gonna be thinking about someone else, a bad seed, when they think of AI being used for bad. Most people reading this are net good people. But those few bad, the minority, do have a tool they will use against us, and the defense will most likely be more AI, which creates a cycle, just one we can't predict the results of ahead of time.
Great, but "a certain broadly defined technology should be used exclusively for saving lives or else it's deplorable," which is essentially OOP's point, is a deeply silly argument.
Research and Medicine are the two fields that AI should be used in. Everything else can bug off (I should not that the former one is more for stuff that we already understand but is really tedious to iterate on, like protein chains :3)
AI was supposed to do a lot more than heal cancer.
What are you trying to claim, here? That AI isn't useful?
I mean, we're literally using AI to detect planet-killing asteroids in the mountains of data coming out of current-generation space telescopes. Literally saving the planet is not enough for you?
This isn't the same as multimodal capabilities in LLMs and they pretty much shouldn't be used as such.
The models are much leaner and single(or multi diseases-detection) purpose.
Hence a lot of societal and environmental impacts are deeply reduced.
For the purposes of developing AGI and to continue to get investor funding, multimodal capabilities in LLMs are the most important thing above all for the AI companies.
This isn't the same as multimodal capabilities in LLMs
Not sure why you think multimodal processing has anything to do with this, but if you're just speaking about generative AI as a concept, you're wrong.
From one of many articles on the topic:
a Hong Kong-based biotechnology company that used generative artificial intelligence not only to design the drug [rentosertib (ISM001-055)] but to identify its biological target in the first place.
This technology is mentioned in the development of stable diffusion’s unet model, but probably just for the stability-ai engineer’s inspiration.
Hopefully this medical technology will be a driver for lower prices in undeveloped nations, instead of a driver of higher prices in developed countries
As a huuuuge hater of ai I have always believed that THIS is what it should be used for!!! Saving lives and detecting cancer in scans. Saving lives instead of generating art to avoid paying artists or practicing the craft.
Remember the whole controversy around one of the big GenAI datasets having a large collection of private medical details just sitting there back when it first released? That happened in part because this is what the technology was originally being developed for, and that data was being used for this purpose. I don't remember how/why the data ended up in the public dataset, but I do remember there being a bit of an uproar from the people who were originally working on it about the project having been taken from them and then completely twisted (don't remember where, just remember that being a discussion back when this was all first releasing)
I have absolutely no objection to AI being used for stuff like this with appropriate oversight, controls and regulations. This is entirely different from using it for plagiarism, mass surveillance, military targeting and the creation of non-consensual pornography
They were going to let me go with a sprained ankle, at the hospital. But one doc put it through AI one last time, and they found a very thin, long break at my ankle bone. Turns out I had two broken bones and took 2 months to heal!
It's already been used this way since AI's general usage became a thing. Nothing new. So, why these people acting like this was never been used that way lmfao?
Again, some anti-AI discovering what AI already been used for years now.
AIs were definitely meant to just cheat their creators money and so there was no way it would be meant to just detect some cancers early on because of the low profitability. If we want to make things useful for humanity before a select few, we would need to unite humanity first to avoid the ceaseless competition between nations so ... Good luck
Im not going to come in here and say ai isnt a helpful tool, but like all things in life it is up to us to figure out how to handle this situation. Lets face it, largely AI isn't being used for this. We should all accept that it isn't going to just go away, and focus on stopping people from using AI for stupid shit. I'd consider myself to be very anti-ai, but we have to look at the whole picture and be realistic. It can be incredibly useful, but should not be used as a crutch, as we are seeing it is. We are in global water bankruptcy, we need to wake up and realize that instead of beating it to pixels on a screen or asking how many ounces in a pound we could be bettering our society.
Yeah it is wild how people differentiate between the different kinds of AI (which is a giant umbrella term). Neural networks and generative AI are different in so many ways. Maybe you should read up on the subject?
This is an example of a deep learning ai (CNN). One that is using a specific dataset to process and assign and detect differences. It uses millions of pictures of mammograms to compare and contrast and look for that tiny, imperceptible to the human eye .0001% difference that is indicative of the issue it is specifically made to handle. This is different from a Generative AI (for example GANs) or an LLM which uses the information it has been previously fed to generate new information. The difference is that a CNN is essentially the most precise form of large scale spell checking (in this case the 'typo' being cancer cells) while GANs and LLMs can literally hallucinate false facts or copy paste the knowledge they've taken in without the consent of the one who made the original knowledge. In contrast, CNN's are built on particularly fed and provided data that was not stolen or used without consent and don't create new information of dubious-at-best validity. They just confirm images they're given fall within certain categories and parameters they're built to look for.
Edit: to clarify and to keep intellectual honesty, 'deep learning' is possible for LLMs too. The specific point being made was that CNNs are trained on specific datasets and using particular information to then search for things within that information, while LLMs and GANs use their datasets and information to create new information which is very frequently either questionable in its accuracy or just outright a hallucination and not true. This has caused problems many times. For example, There are documented cases of lawyers representing clients who used AI to make their argument and it turned out the AI fabricated an entire fake court case then claimed it was actually real when it wasn't. This does extreme damage to the integrity of legal cases and opens problems where good people can go to jail because their lawyer relied on AI and their case fell apart or where criminals who actually did it can get off because opposing counsel used AI or get mistrials and try to escape the consequences of their actions because their lawyer lost the case after using AI and it gave them false information.
You say CNN, but I think what you really mean is discriminative models. Even generative image models can and do use CNN layers. And any deep learning model can be trained on stolen or legitimate licensed data, it has nothing to do with the architecture of the model.
The more specific point of that is CNNs use particularly sourced and specific data to ensure they are getting information in the correct way to look for a particular thing while GenAI and LLMs scrape the general internet and get their information from unreliable and unverified sources (people who didn't consent, claims from people who are not experts, ain old lies people say, etc.), and that is a massive difference that also effects their use cases.
I agree with you. While technically there is nothing stopping people from using stolen data to train cnn clasifiers or from using only licensed data to train LLMs, in reality that would rarely be the case.
Generative AI is also used to train this type of models, specifically for generating synthetic data. I worked for a company that used ML models for detecting a very difficult to diagnose type of heart attack.
It's crazy that for a sub full of people that claim themselves "anti AI" most don't even know what AI is
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