"AI will cure cancer" misunderstands both AI and medicine
rachel.fast.ai
rachel.fast.ai
Even though we know prognosis is much better for cancer, and many other diseases, if you catch it early, we do essentially nothing to catch it early. My understanding is that this is because:
1. Administering regular MRIs, blood panels, etc. is expensive, in terms of the initial data collection
2. It’s also expensive, in terms of getting healthcare professionals to analyze the results
3. People often get the analysis wrong, in terms of both false negatives and false positives
4. False positives can lead to even more scans, analysis, etc., costing even more money
It does seem possible to me that specialized AI could get much better than humans at interpreting this data, doing it very cheaply (solving problem 2) with far fewer false negatives and false positives (solving 3 and 4). And it’s even possible that AI powered robotics gets great at collecting data in the first place, bringing down the cost of problem 1.
Basically, “AI invents cures for different types of cancer” seems like a moonshot, but “AI makes proactive medical scanning cheap and effective, thus greatly improving cancer outcomes” seems like a real possibility.
While we have some proactive screening for some types of cancer, the status quo for many types of cancer/patients is “wait until the cancer has spread enough that the patient is experiencing significant symptoms, with no systematic way to detect cancer early.” This is clearly not great. We’re accepting this for practical reasons today, but I do think AI has a significant chance to greatly improve the status quo here.
This is, to some extent, misleading.
I mean, earlier treatment is beneficial, but there's a significant confound. All else being equal, if a cancer is less aggressive and slowly growing it is more likely to be detected early.
Put in other terms, the cancers detected earlier by screening are a very different population of cancers detected late and with progression.
Wow! That makes so much sense! I had never considered this!
'Survival' for cancer tends to be defined as surviving 5 years. The earlier you catch, the more patient had left to live anyway.
The basic good old medical care invented 100 years ago, while dizzying amounts are spent on prolonging lives for very, very few years, often very late in life - efforts that are very close to - in effect to have done nothing, ie. almost performative.
Is this true?
My grandma had a melanoma at the age of 74, which is "old" by most human standards. It was located on her earlobe and an operation helped her get rid of it.
She then lived to be 90, most of that extra time either fully or partially self-sufficient. Only in the last months in her life she really deteriorated.
Basically, she gained almost a fifth of her life by that single operation performed when she was already old.
But because of way our system works, we’ll happily pay $300k to hospitalize an otherwise healthy 70 year old who is dehydrated and develops serious problems that could be solved by an aide or helper that would cost $20-30k.
there's probably no way to actually do anything concerted about it without turning society into Logan's Run but having gone through it with a grandparent and a parent, it is clear something is broken at the end of life
So yes, it's true (although that includes the cost of hospital stays which is where a lot of people end their life).
I’m not sure what you mean by “very, very few years”. As a hypothetical would prolonging life for ~3-7 years in a 40-50 year old be considered “almost performative” to you?
“Good old medical care” often means 3-6 month survival for these patients.
My late wife detected a mole that was melanoma in 2019. She was within months of being cleared for observation in 2023 when two brain tumors were detected. Despite the best of care, she was gone in 6 months.
If her initial treatment had been in 2024 instead of 2019, it’s 80% likely she would be around for another decade or more. That’s how fast new treatment options are coming to market, and data analysis with AI and other tech is improving it. New trials are using platforms like Moderna to provide custom vaccines that should reduce treatment side effects.
While the hyperbole of the media is annoying, the impacts of new tech to identify genetic vulnerabilities in cancers is near miraculous.
You have to advocate very heavily. With melanoma, I wouldn’t mess around and seek at a minimum second opinions from the nearest major cancer center.
I was speaking specifically towards screening more and detecting earlier. They have utility, but recent evidence seems to indicate that it's not nearly as much as the public assumes.
And at the end of the day "cancer" is a category, not a thing. Sometimes (prostate cancer) early detection and intervention is bad, as the cure is worse than the disease! Other times (ovarian cancer), accidental early detection while looking for something else entirely, as symptoms don't present until you've hit Stage 4 typically.
The problem is, this is hard to measure. We know that "detected early" correlates with better long term outcomes. But "early" means "smaller and with less spread" which in turn is strongly correlated with "growing slower and spreading less".
We've had unpleasant surprises where e.g. extending screening to earlier ages detects more cancers but doesn't decrease the number of people dying from that type of cancer because of these confounds.
For the other point, personally I don’t really buy the argument of “it’s better not to know you have cancer X, because it might end up being low impact.” If we had excellent regular screening, yes detection of low impact cancers would become a lot more common, but I think people’s perception of them would change too. If it became a common thing for cancers to be detected, but the detection could reliably say “this is likely low impact, we should just keep an eye on it but not treat it”, this would be a lot less scary. It would become normalized IMO. Cancer diagnoses are partly so scary right now because we’re often mostly catching cancers that have progressed and are causing symptoms, so the public perception is rightly “cancer diagnosis = very scary.”
The harm is from investigating the screening test result and not the test itself.
> If it became a common thing for cancers to be detected, but the detection could reliably say “this is likely low impact, we should just keep an eye on it but not treat it”, this would be a lot less scary
This is already the case for some like prostate cancer and certain lymphomas.
> Cancer diagnoses are partly so scary right now because we’re often mostly catching cancers that have progressed and are causing symptoms
The most aggressive cancers are also the least likely ones to be diagnosed by screening due to growth rates, screening intervals and diagnostic test limitations.
So already before investigating the result, there's a very real consideration whether increasing the number of colonoscopies is likely to be a net benefit.
Most if not all of the complication/risk (perforation and major bleeding being the ones of note) is from the polypectomy / biopsy part of the colonoscopy.
Finding type 1 diabetes this way in a young teenager was so absolutely out of the norm that a major children's hospital had no idea what to do with her. They admitted her because it was protocol but it was completely unnecessary and we had to explain how it happened at least ten times while we were there.
It was an eye opening experience.
If those tests are done on demographics where the chance of a true positive is also very low and the difference in risk profile between catching it during such screening vs. waiting until the patient discovers it is not very significant, it can take a very low rate of complications before it becomes a problem.
But, yes, we do limit that, and that is a major reason there are very few mass screening programs.
5. Diagnosing cancer/disease early does not necessarily improve outcomes.
https://en.wikipedia.org/wiki/Lead_time_bias
https://en.wikipedia.org/wiki/Length_time_bias
> we do essentially nothing to catch it early.
Huh? That’s the whole point of cancer screening which we do a lot of in the West. The benefit of which remain hotly debated. New tests are also constantly being researched.
> While we have some proactive screening for some types of cancer, the status quo for many types of cancer/patients is “wait until the cancer has spread enough that the patient is experiencing significant symptoms, with no systematic way to detect cancer early.”
Maybe it depends on where you are, but where I am (Vancouver BC, Canada), the above is true. Proactive cancer screening is quite limited here. I believe it's limited to screening for cervical, breast, colon and prostate cancer, plus lung cancer for 55+ year old smokers who smoked for at least 20 years. And for even those specific cancers that are screened for, availability is limited by risk factors like age, e.g. you can't get screened for colon cancer until you're 50, that sort of thing.
There are so, so many other types of cancer and non-cancer diseases/conditions that we do not screen for at all. Plus, even for the cancers we do have screening for, it's often not frequent enough to catch more aggressive variants early - a lot of these screenings are only once every ~2-5 years. The idea of, say, proactively taking MRIs, blood panels, etc. on people, looking for early stage cancer (and other conditions) throughout the body is not something that's available. You can't even get an annual physical with a family doctor anymore, there's only screening for a handful of specific diseases, and only once you reach certain ages/risk factors.
Cancer screening starts a bit earlier for women, due to higher risk of breast and cervical cancer, but if you're a man under 50 in BC, you're really never getting any sort of medical test done ever (even simple things like blood panels) unless you go in to a doctor's office for a specific condition. I have MANY friends and family members who've been diagnosed with cancer in Canada, and almost none of it has been caught in regular screening, because the screening is so limited, its almost always been caught by the cancer spreading enough that the person goes to their doctor due to symptoms.
Screening intervals are based on doubling time.
> The idea of, say, proactively taking MRIs, blood panels, etc. on people, looking for early stage cancer (and other conditions) throughout the body is not something that's available.
You can pay out of pocket for a “screening MRI” in BC but from my clinical practice the yield is dubious.
> You can't even get an annual physical with a family doctor anymore,
Evidence has shown the physical is useless.
> there's only screening for a handful of specific diseases, and only once you reach certain ages/risk factors
Screening needs pretest probability and a diagnostic test with sufficient accuracy. It simply does not exist for most cancers. Trials are underway for new tests like cfDNA but in 2024 there aren’t any validated options.
What about AlphaFold? It’s just one piece of a very large puzzle but it’s not like AI needs to do it all.
I don’t think AI will be a complete solution for much of anything but I do think that it will be a part of the solution for just about everything.
There are many things broken about our world. We should work hard to fix them. But the promise of AI as a research tool to create the kinds of breakthroughs that humans aren’t capable of is undeniable.
Footnote: I think the context of her thinking is very much around the categorization and management of patients, which doesn’t necessarily relate to “AI will cure cancer”.
The point of the article is that AI as a research tool is insufficient to result in improvements to patient outcomes on its own. The article includes, for instance, the example that better MRI interpretation doesn't help those people that are being refused an MRI.
Rachel and I quit our jobs and spent years, entirely for free, helping make AI more accessible to more people. We did that because we think AI is great! Pointing out that helping patients requires more than just AI is not anti-tech, it's pro-human.
We can care about both the technology and the context in which it operates.
Nevertheless, I can't help but think that you are seeing this issue to negatively.
"The point of the article is that AI as a research tool is insufficient to result in improvements to patient outcomes on its own." This seems unlikely if you consider this question as-is. Past technological improvements has made healthcare better overall without requiring societal changes necessarily. Take mRNA vaccines, a technology that has improved Covid-19 outcomes tremendously. Sure, certain groups have less access than other groups, but surely even marginalized groups are better off overall because of the existence of these vaccines. Health is not a zero-sum game.
And I think this negative attitude also misses the potential of AI by default. Yeah it sucks that not everyone gets MRI access, but those that do will benefit, including marginalized groups. I guess it feels wrong to some people to express a positive sentiment at an unjust state of affairs, but improved diagnosis and treatments translate into lives saved.
You also have to compare AI to the status quo. Sure, AI will have biases, but so do humans (as Rachel points out!) and the decisive question is whether AI has less biases (similarly to how safety of self driving cars is judged). With AI you at least have a chance of analyzing the decision making, and making it objective. We should be extremely excited about this possibility!
Aren't those two entirely orthogonal issues though?
I expected the article to be about how people misunderstand the difference between knowledge and intelligence. No matter how smart AI is, it can't just magically invent a cure for cancer - it has to gather the knowledge of how things interact and what effects drugs have.
Still, AI does have lots of potential to improve things there. My wife is a Research Associate in a biotech startup, and she could basically be replaced by humanoid robots to run experiments (probably don't even have to go that far - there are cloud labs already) and AI to run analysis. The analysis will be much faster and the robots can work 24/7, so you could really increase throughput of experimentation.
edit: It is clear that, even at the current stage of decent summarization, this is transformative tech that can 10x current workflows today.
- "AI is used to disproportionately benefit the privileged while worsening inequality"
- "the goal is to increase corporate and government revenues by denying poor people resources"
- “It is a pattern throughout history that surveillance is used against those considered ‘less than’, against the poor man, the person of color, the immigrant, the heretic. It is used to try to stop marginalized people from achieving power.”
- The same pattern is found in the role of technology in decision systems.
- "The goal of many automated decision systems is to increase revenues for governments and private companies"
- "here is already a clear pattern in which AI is used to centralize power and harm the marginalized."
Few people on HN can read this kind of critique without short circuiting but it’s a valid critique and I would bet a good sum that if you looked into this author you’d find lots more direct evidence of promulgation of leftist ideology (critical theory)
Good intentions with bad outcomes are still bad outcomes. Ignoring the trends is to ignore those marginalized by them even more.
The article largely deals with unfortunate side effects of a combination of feedback loops, implicit bias, economics, and lack of technical understanding in the broader community -- quite the opposite of any "conscious decision".
Second, AI is used to disproportionately benefit the privileged while worsening inequality.
The article talks a lot about the challenges of care delivery and how there appears to be systemic breakdowns in how patients are listened to and how demographics seems to lead to worse outcomes. These are all serious issues. It further states essentially that AI, at least learning based AI, learns what it is trained with and most training data indirectly encodes the various social biases that influence the data collection or what is collected in the data. The is true too.
However neither of these have to do with AI curing cancer. They seem more statements that AI won’t solve all social ills, which is absolutely true. But these don’t speak to given a positive cancer diagnosis can AI provide a route to curing an individuals cancer. I suspect the answer is “maybe,” but none of the social and political points made are why. It’s because cancer is very complex and we need a vector that AI can generate some solution in to treat any specific cancer. Since there are many many types of cancer and many many variants of those types, as well as per individual cancer genetic variability, it seems unlikely “AI will cure cancer,” but I think it’s very likely AI will make cancer treatment much more personalized, discover many new therapeutic agents, and accelerate human driven research. It is already used in generic immunotherapy, mRNA design, and other treatments. As tools and techniques become better, as well as our understanding of how to apply it, AI will help a lot.
The recent Google Gemini image fiasco seems like a rather solid example of this being real.
The problem was that the calculation for housing (not food) welfare benefits changed and the migration to the new software that came with it went poorly.
But I find these arguments much easier to understand if you look at it through that lens. Of course magic can cure cancer because… it’s magic!
The problem is that this makes almost anything sound reasonable, besides things that violate the laws of physics.
I don't think that thinking this far out is anything but pure speculation. Whether the mechanism is human intelligence, AI, or magic.
There is no evidence that AI that's useful enough to help make research breakthroughs can be replicated orders of magnitude more quickly and cheaply. Whilst that's a true fact about software, AI is not just software -- it requires a lot of hardware. And currently it's far less efficient at using that hardware for general purpose problem solving than humans are.
For example, reading an MRI or other medical scan correctly goes a long way toward curing cancer. Reading it incorrectly wastes precious time as problems are ignored or mistreated with the wrong methods. I knew someone whose bone cancer was mistreated as a rotator cuff injury for a little over a year due to the fact that an inexperienced and probably overworked doctor did not correctly identify it in the slew of tests and scans the patient had taken.
In the future, it is likely that AI will always read these scans and test results more accurately than a human physician, leading to higher remission success rates. This will happen fairly quietly and behind the scenes, even if AI doesn't invent the magic cancer pill.
I don't think it's a coincidence we're seeing the largest numbers ever of people leaving organized religion at the same time we're seeing so many communities like the one that's grown around AI spring up that are essentially religion without the historical baggage.
Of course, at the lowest levels it’s entirely understood but it’s the emergent properties that give many the feeling of magic — and I would argue quite reasonably so.
…for people who use ‘magic’ to refer to things that accomplish complicated tasks whilst abstracting it all away so that it looks easy (much like how I can copy and paste between my iDevices on a LAN and it magically works), anyway.
> Of course magic can cure cancer because… it’s magic!
If you take magic to mean something supernatural, then yes. It’s essentially of the same form as ‘god is perfect; a god that exists is greater than a god that doesn’t; therefore god exists’-type arguments.
If the info you need lies in genomics but all you have is images, no amount of cognition can bridge that gap.
AI has seen some success in detecting disease (diagnosis) but none at all in creating new treatments or cures. Future use of AI likely can help guide or optimize an existing therapy (e.g. chemotherapy) by detecting or discriminating feedback faster than a human can. But invention or discovery? No. AI as we know it today has shown no ability to advance the knowledge frontier beyond what the facts fed in by its teacher, nor any signs it ever will.
Your critique might apply to an article written about the use of AI/ML in science, in which case it would be uninformed - algorithms like DeepVariant, DeepConsensus, and AlphaFold are fundamental AI-enabled tools for gathering and interpreting new information from existing sensors that changed the state of the art and are advancing science and enabling cures today. AI-enabled tools are also improving information management and literature search for scientists, because advancing science usually is about making better use of the info you have - a lot of scientific breakthroughs today are made by analyzing data that has already been gathered (like Genbank or UK Biobank or All of Us data).
I think you’re jumping the gun here, this paper was a hot topic when it was published. Patient symptoms combined with objective data is already the medical standard.
Note that:
1. KLG is not a measure of pain but of OA radiographic severity.
2. KLG 3-4 is not a prerequisite for surgery.
From the article:
> While radiographic severity is not part of the formal guideline in allocations for arthroplasty (which only requires evidence of radiographic damage), empirically, patients with higher KLGs are more likely to receive surgery.
TKA patients skew to higher grade for many reasons, one being that studies have shown KLG 2 patients who undergo TKA are more likely to experience dissatisfaction (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344222/).
There are a lot of “ifs” in this paper which did not examine whether KLG 1-2 but ALG-P 3-4 patients benefit from TKA over conservative mgmt or other surgical interventions. It’s also unclear whether this better selects patients for TKA than KLG 1-2 + pain scores and other clinical variables.
All this shows is that KLG is a poor correlate for pain, which is known and not what the score is designed/used for.
Your sober assessment seems valuable, and would make for an interesting letter to the editor.
1 = https://www.thelancet.com/journals/landig/article/PIIS2589-7...
Not sure what a letter to the editor would accomplish. The nature paper only interpreted radiographs and the only claim of the authors was basically that the model is a better predictor of pain than KLG.
Your comment misinterpreted this as “using the patients' symptoms and objective data” (when they only used objective data) and added “may actually outperform current medical standards” which was not the claim as current medical standards already consider patient symptoms in addition to objective data, as stated in the article reference to the TKA guideline.
When I report a joint xray I’m not assessing the patient’s pain level, they can be asked that.
> Your comment misinterpreted this as “using the patients' symptoms and objective data” (when they only used objective data)
This represents an important misunderstanding of the methods of the paper. The model was trained using images (objective data) and the pain score (patients' symptoms). From the methods: "A convolutional neural network was trained to predict KOOS pain score for each knee using each X-ray image."
Also with respect to the author's claims, from the paper's abstract:
> Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
You think I'm misinterpreting, but I still think that the paper is more important than you're giving credit.
In this context we are talking about a pain predictor from an xray which is neat but not the point of KL grading.
KL is a system to grade severity of osteoarthritis on radiographs (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4925407/) and not a threshold for surgery or predictor of symptoms.
The comparator, current medical standards you reference, would be a model outperforming surgeon assessment in conjunction with radiographic findings. Not the predictive value of KL grade.
Too bad this was in the last paragraph or so, it could've spared me from reading the rest if it was in the beginning.
Not the best article really.
Otherwise also a mediocre, rather incoherent piece especially for someone with a PhD.
This is not that.
very misleading title given how diverse AI applications are in medicine.
It’s the lowering of cost that feels like the revolution in healthcare. AI should enable nearly free ways of mining noisy signals in the body could catch issues. Smart toilets, mirrors, scans, etc. all help.
I work in the emergency department of a busy hospital. MRIs are pretty labor intensive to perform and take 15m to an hour. We are not going to get to a point where we are regularly scanning people with MRIs without clear symptoms of CVA etc. They require techs to run, you aren't going to remove much "expensive human labor" other than making initial radiology reads faster. The article makes the distinction that someone needs to decide to order those time-consuming, expensive scans, and that is where the point of failure is right now, in that we sometimes write patients off as delusional. AI can't help in situations where we have no data.
She is a wonderful clinician who has enormously helped other patients with complex diagnostic paths and with diagnoses that are severely neglected.
I keep coming back to the fact that if you asked even GPT 3 how to solve climate change, it would give you a perfectly good answer. We have this idea that AI will “solve climate change” at some point in the future. What we really mean is “give us a different answer to climate change that has zero cost”.
Climate change is not currently being solved because of politics and existing systems, not because of a lack of intelligence.
There are so many similar situations in medicine. Intelligence is not enough- you also need a system capable of acting with that intelligence.
AI could be transformational, but not without systemic change to support it.
> Climate change is not currently being solved because of politics and existing systems, not because of a lack of intelligence.
It's a technical problem that's sociopolitical because we don't have a Pareto-improving technology to solve it with. Like 50x fusion reactors. Not a complete solution, but with it, the political will to shut down remaining emissions is easy to muster.
Some people think we'll have AIs soon for whom "design me a fusion reactor which is 50x cheaper per kW to deploy than solar" is the sort of input which gets the requested output. I am skeptical of this. But it isn't an incoherent thing to believe.
Where cancer is concerned the situation is much less clear.
IMO, pretty much magic wishful thinking.
I'm enthusiastic about AI. But it's not magic. The main problem here is as Feynmann said, "For a successful technology, reality must take precedence over public relations, for Nature cannot be fooled".
In this case the problem is that a fusion reactor is a real, physical machine intensely dependent on the real world in a million ways. From the actual physics of fusion, to manufacturing capabilities, to the capabilities of the various sensors, actuators, processors, etc needed to control the reaction.
You can't magic that up with AI. There's no way for it to figure out that we're subtly wrong about some fact about fusion and that we can get 100X better by just doing things differently. A hypothetical GPT20 would still need to actually perform real physical experiments to gain such knowledge, because it'd be nowhere in our books or the internet for it to ingest it.
That just kicks the can down the road. If our general strategy (aim for exponential growth in all things) remains the same, cheaper power will mean we use more of it. Even if we completely phase out CO₂-releasing energy sources, the waste heat of our industrial processes would eventually dominate the power received from the sun. Even if we solve that… deuterium, like oil, is non-renewable. Doesn't matter how cheap it is: there are only two dozen trillion tonnes of it in the ocean, and once we've run out, we've run out.
He then asked what it would say for the prognosis. Also again spot on.
So there’s things it can do well, for certain. I’m of the opinion that if we manage to scale this machine it will create novel science.
In any case, the two complaints expressed in the article ( inappropriate comprehension of patient problems and inequality) are both actually better with LLMs. The patience and understanding of an LLM cannot be beaten. Once we fix the context window, and once again unshackle this machine from ridiculous chains that safetyists have put it in, we will improve patient care.
Maybe they won't cure cancer, but a host of problems will be taken care of.
Embarrassing. Hope somebody hugs you today.
The 'what AI is trying to solve is not the problem' viewpoint of this article is also in the same vein, in that current AI discourse does not account for our society and systems.
Unfortunately as a non-specialist techno-optimist, I can't help but view it as a problem to be solved.
only in spewing obviously leftist talking points.
Here's an example of youtuber [0] who got helped by Ada[1] which suggested she might have endo given her description of symptoms which countless doctors failed to narrow down.
[0] This calculation tells you your chances of being sick https://www.youtube.com/watch?v=KzA9VATcZhY
[1] https://ada.com/
Better statistical models will help us identify diseases, and maybe molecules we can use to fight those diseases.
But "AI" is not magic sauce you can slather on to a problem to get a solution. It's going to take statistics-savvy medical professionals, and medicine-savvy data professionals, working together. We've complicated things to bring solutions within reach that might've not been within reach before, so we're going to have to be smarter about how we apply these new tools.
We are far from AI that models reality.
Wonderful book on it https://www.amazon.com/Weapons-Math-Destruction-Increases-In...
https://www.technologyreview.com/2023/10/10/1081351/are-we-r...
> Second, AI is used to disproportionately benefit the privileged while worsening inequality.
That's bullshit. Computers and other gadgets always start out as toys of the rich, but they quickly trickle down to everyone else. Compare the multi-thousand dollar cell phone of the 80s use by the rich to the multi-dollar cell phones of today used by billions. I don't mind if billionaires spend their money on AI now so that the R&D results in cheap AI in a decade that can be used by everyone.
The author also goes and lists people harmed by computer problems without provide a benchmark of the number of people similarly harmed by human problems.
But is this a function of moore's law? I have to say that OpenAI's training costs seems to be growing exponentially, not declining exponentially.
Cambodia in 1977 was a much more equal society than America in 2024. Can't argue with that.
When they do trickle down often it can happen as a monopoly technology where supply is controlled by a few powerful entities.
https://www.science.org/content/article/giant-project-will-c...
I think these general statements are fine. I believe that AI will cure long COVID and ME/CFS soon.
I have already seen AI predict these illnesses with outstanding accuracy with no known biomarkers identified, drugs and molecules being found to neutralize it, and even new parts of the virus being identified to target.
I feel like this is the most exciting time to have one of these illnesses, cancer included. We may indeed find a cure for everything. Or at least everything we amply fund with billions of dollars.
We just entered the golden age of immune-cell therapies. I just can’t even imagine what we will do in the next ten years.
It's easy to say "X will do Y" when X is a force multiplier for things we already do.
The fact that it could also come up with new ideas is a bonus on top of that, making it even easier to say.
Today’s AI won’t cure cancer, but we have no idea what the AI of 20 or 200 years from now will be able to do.
Or Sam Altman pumping his fund.
I mean on the surface it seems like the answer is “yeah, probably”, but you’re saying nothing of how exactly.
When we say "She has cancer" we mean "a cancer". (Hopefully she doesn't have all of them!). Why couldn't the sentence "AI will cure cancer" mean "a cancer" as well?
When you say "AI will cure cancer" that is not a singular event, so you assume cancer in the plural. You would have to say "AI will cure some forms of cancer" if you didn't mean the plural here.
- Grab a cancer (or virus, bacteria, etc.)
- Sequence it
- AI will develop a custom therapy for that cancer
In broad strokes, it's not hard to develop a therapy for any specific cancer or other disease in a specific individual. There are several broad strategies:
- A targeted, custom phage to kill a bacteria (or extrapolate to killing a type of cells)
- A custom vaccine to make your body make antibodies specific to a disease
- And so on....
This is a ≈2 year research effort to do in each case, and perhaps a ≈10 year validation effort, not to mention regulatory. By that point, the patient is dead, or AIDS has mutated a few dozen times, and regardless, you need a massive research team to do so. And to do so, you've spent many million dollars on a research team that whole time.
"AI will cure [X]" consists of AI doing the same thing instantly. I go to a doctor. My chronic disease is sequenced. My specific immune system is encouraged to attack that specific disease. I'm cured.
(And yes, we each have a very different immune system; see MHC for an example of how and why)
How? You’re hiding a ton of complicated work in these 2 words
> AI will develop a custom therapy
This statement suggests you really don’t know what you’re talking about with regards to AI.
AI doesn’t develop treatments magically. Work needs to be don’t to curate a dataset of treatments and diseases, BUT even then AI can’t create new treatments for existing untreatable cancer as we don’t have any data to go off of.
At that point, a team of doctors might as well analyze the data themselves (probably using a more specific kind of ML technique)
You’re too cavalier in hand waving away the real work by saying things like “AI will do this. Ez. 2 years”
IIRC, it became a term of art during image classification AI development, where an AI might confidently assert a car was a potato, and the name stuck.
But there can be useful hallucinations.
Not sure about the origin of the word, but I always thought it was marketing when chatgpt and dalle was brand new
Simply making an informed guess and extrapolating to data outside the training set (whether that informed guess is is correct or incorrect) is not hallucination.
DNA sequencing has been following a Moore's Law style curve. It is cheap and easy now.
> > AI will develop a custom therapy
>
> This statement suggests you really don’t know what you’re talking about with regards to AI.
>
> AI doesn’t develop treatments magically. Work needs to be don’t to curate a dataset of treatments and diseases, BUT even then AI can’t create new treatments for existing untreatable cancer as we don’t have any data to go off of.
No one is suggesting it can. AI is very good at pattern-matching. There is a cookbook of techniques here:
1) Create a phage which is very good at injecting into a specific type of cell
2) Create antibodies which can latch onto a specific type of cell, virus, or cancer, so the immune system can attack them
3) Create a vaccine, which is much the same as the above
None of these are hard in of themselves. What is hard is that there isn't a virus called "AIDS" or "flu" or "cold," but a very, very large family of viruses. Ditto for cancer and bacteria. This is the exact type of pattern matching problem ML excels at. Curing a specific virus isn't hard; what's hard is because of all the variations. That kind of adaptation is exactly what ML excels at.
Once covid was sequenced, the actual creation of a vaccine took -- literally -- a couple of days (of work by the world's best scientists). What took much longer was validation, regulatory approval, getting manufacturing up, etc.
> You’re too cavalier in hand waving away the real work by saying things like “AI will do this. Ez. 2 years”
You're attacking a strawman here. Step zero of this process will be:
- Collect a dataset of bacteriophage DNA and of bacteria they're good at attacking (this is a massive undertaking)
- Something very similar with DNA and antigens (much of this exists / has been done, but was a huge undertaking; see "protein folding")
This is a few years in itself. That's when we can start to begin training an AI. There are many other similar-sized steps along the way. "AI will cure cancer" doesn't mean "AI will cure cancer tomorrow." However, I can see all the steps along the way, and no fundamental hurdles.
It's like the Apollo Program or the Manhattan Project on day 1. Yes, it's a major undertaking, but there's every reason t believe it will work. That's exciting.
So far, aside from calling me an idiot, no one in this thread suggested where the flaw in the above lies (and none of the comments suggested the poster had any understanding to do so). I responded to your comment since it was closest.
1) Understanding the specific mutations
2) Helping the immune system find way to identify, and therefore attack, those specific cancer cells
More of the work focuses on t-cells, but otherwise, it's not too dissimilar from the work on infections.
I should know better than to discuss medicine on a SWE forum. Every post here starts with an insult. Not a single post contains any technical detail, nor even clues that people even understand the words I'm using (t-cell, MHC, etc.). It's like arguing with a cross between a five-year-old and a teenager who knows better.
I think I'm done here. There is no point.
Cancer is not "a" mutation, and that is the whole problem.
You are talking about personalised neoantigen-specific t-cells as a generic cure for cancer, while ignoring the fact that not all generations of a cancer express neoantigens, or even the same neoantigens.
Ergo, it is a therapy, NOT a cure.
I can't imagine us ever coming up with something to help us with the fact I'm ignoring. It totally doesn't sound like the sort of thing deep learning would be at all good at.
/sarc
That fact you are ignoring is the whole point about why engineered t-cells cant be a cure. No neoantigens means nothing to target. And your solution is to just wave your hands and say "deep learning will figure it out"...
You truly are a clown.
Asking Stable Diffusion for a picture of the chemical structure of a drug to cure even one specific unsolved cancer… I'd be surprised if that ever works (but given how crazy the rate of change has been, only 2σ of surprise).
the question is whether the cost of using AI to generate a solution, and then fixing it manually, will be lower, or higher, than building the solution using humans.
as we have seen before with wizards and other tools to generate solutions, the cost of fixing the solution is often times higher than it is to just build it from scratch manually.
also, as humans use AI more and more, there will be less and less material to use for training that is high quality, and AI will train on itself, leading to a rapid decline in quality of the output.
That's trivially also true for humans, you can't get out of your brain something that isn't in it.
If you meant "external training set", then it's false for both AI and humans, as demonstrated by e.g. AlphaZero getting superhuman performance despite zero examples of human games of Go or Chess.