[frognumber]

I kind of feel like that's exactly why AI is helpful 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)

[supermatt]

I think this is you falling on the "misunderstands medicine" part of the equation.

[bratwurst3000]

It sounds so easy maybe you could be so kind and write a few lines of codes and finally cure cancer

[ben_w]

Reminds me of this: https://youtu.be/tNfGyIW7aHM?si=vS4vlmD0mufCv0Cw

[dartos]

> sequence it

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”

[ummonk]

No, you're the one who doesn't understand AI. At this point AI has demonstrated ample capability to extrapolate to out of sample data.

[e44858]

Also known as hallucination

[ben_w]

No, hallucinations are specifically when they make mistakes, even when they're more like interpolation than extrapolation.

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.

[dartos]

Idk maybe splitting hairs a bit, but I’d call any output that isn’t represented or which goes against data in a training set is a hallucination.

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

[ummonk]

I'd only call something a hallucination if the AI claims the existence of data that doesn't actually exist.

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.

[frognumber]

> How? You’re hiding a ton of complicated work in these 2 words

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.

[supermatt]

You really don’t understand what you are talking about. Cancer is not an infection. You can’t sequence it and treat it as you would a pathogen.

[frognumber]

Cancer is a mutation. Much of the most promising recent work I've read on therapies focuses on:

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.

[supermatt]

You may be done here, but you are still misunderstanding the medicine. You are making exactly the same errors as mentioned in the article - generalisation.

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.

[frognumber]

Gosh. You're right. To solve that, we'd need something which could somehow predict the overall set of neoantigens present from the sequences of a few branches of a cancer, and not only that, to be able to rapidly adapt therapies to the antigens present. It sounds like we'd need some kind of algorithm which can do very complex pattern-matching and generalizations.

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