As an MD with a special interest in statistics, color me skeptical. I'd love to be proven wrong though, so please provide references.
Edit: yeah, so the way this whole thread is developing really goes to show (yet again) that medical AI hype is relying as strongly as ever on the fantasies of people who've never seen any clinical work.
Cool! Which hospital is that? Is the clinical staff happy with the results?
Personally, I've never seen any medical ML application that made my job easier. But it would be nice to see.
I can answer that: close to zero. Clinicians don't want stuff that makes recommendations, as good as they may be. They want a bycicle for the mind: something that helps them visualize, understand the big picture and anticipate better. And also ensure that trivial stuff to do is not forgotten (now that's the place a recommender engine could fit in). That's a fundamental misunderstanding of what a clinician's job is that is unfortunately very common.
What do you ask of your software tooling? Do you want something that just tells you what to write? No, you want a flexible debugger. A compiler with precise error messages. You want a profiler with a zillion detailed charts allowing you to understand how everything fits together and why such and such is not the way you anticipated. Same thing for medicine until the day machines will actually do better than humans, which is not tomorrow nor the day after.
How does epigentics complicate this further, is it that it wides the number of inputs into a biochem system
As far as what we don't know, I'm not sure there's a list. Lack of knowledge implies lack of awareness. I can offer one example: We don't know much about the processes by which collagen fibers are grown and assembled into μm- and mm-scale load-bearing structures in tendon, ligament, bone between embryo and adult, particularly in mammals. Or the extent to which collagen fiber structures are capable of turnover in adults; healing might only be possible by replacement with inferior tissue such as scar.
Personally, I think the complexity of biological systems, and the difficulty of observing their components directly when and where you'd want to, means that they can only be understood with the help of machines. Not necessarily using convolutional neural networks though.
So observing that gene X impacts biochemical pathway in some way Y is already really difficult when there are tons of other genes at play. Add on the fact that these genes could be triggered to stop expressing themselves in certain conditions and it makes the whole process of figuring out what is really going on that much more difficult. Even if we can make some observation, there are tons of contextual situations which would potentially invalidate that observation.
It'd be really hard to train a computer when to stop digging because there's nothing find, or when to keep digging because this patient really doesn't feel like a psych case. And the tests and doagnostics aren't without risk and cost.
I've had a greybeard doctor in my personal life that somehow read between the lines and nailed a diagnosis despite my primary symptoms being something else entirely. (I had recurring strep tonsilitis for months and yet he just somehow knew to step back and order a mono test. It came back negative the first time, and he knew to have me tested AGAIN, and lo and behold it was positive.) None of symptoms were really consistent with mono. I tested positive for strep each time and antibiotics would clear it.). Thankfully I happen to be allergic to the first line antibiotic because if you give amoxicillin to someone with mono they'll get a horrible rash all over their body in like 90% of people.
(To be clear, my argument wasn't that ML isn't useful -- but rather that individual lone hackers are less likely to be using ML to achieve superman-type powers than I originally thought. Supermen do exist, but they are firmly in the ranks of DeepMind et al, and must pursue projects collectively rather than individually.)
For a single individual to have "superhuman" impact with ML, they need not only generic ML knowledge, but also specialized knowledge of some domain they want to impact. Actually, because ML has become so generic (just grab a pre-trained model, maybe fine-tune it, and push your data through it) a very shallow understanding of the fundamentals is probably enough, and in-depth domain knowledge much more important.
That doesn't mean generic ML research isn't important, it's just that it has an average impact on everything, not a huge impact in one specific area.
(I suspect many hobbyist ML projects are about generating entertaining content because everyone has experience with entertainment, even ML researchers.)
Of course policy activism is far less sexy than building new shiny things, so there's little interest in that.
Imaging based diagnosis could read presence or absence of particular gene mutations from the images so that the genes can be silenced by the drugs.
Imaging based diagnosis could also figure out whether a particular cancer precursor is going to develop into invasive cancer and do it better than the experts we have now (otherwise we wouldn't use the AI).
This can also be done cheaper than paying consultants to figure it out and it can be done in locations where they don't have the specialists.
Some companies working in the field (some already have tools approved for use on patients):
https://analogintelligence.com/artificial-intelligence-ai-st...
>Imaging based diagnosis could also figure out whether a particular cancer precursor is going to develop into invasive cancer and do it better than the experts we have now (otherwise we wouldn't use the AI).
Where is the evidence for these claims, other than a VC hype sheet? Like real clinical trials. These claims also show a fundamental misunderstanding of what this data can tell us. Imaging data doesn't give you tumor genetic profiles. It can give you tumor phenotype, which is associated with specific mutations. To get the true genetic profile you need to do deep sequencing at tens of thousands of dollars per tumor, and even then you have the problem of tumor heterogeneity, which lets the cancer evade the treatment.
A major concern I have working in this space is that we're selling people on grand promises of far off possibilities rather than what we can actually deliver right now.
https://www.nature.com/articles/s41591-019-0462-y
Of course changes in the genotype that impact the phenotype enough to influence the disease also influence the morphology of the cells.
But this is area of active research so you can't expect phase 3 clinical trials. Yet.
EDIT: here is another more "perspective" paper how such tools could be used and integrated in current processes, from the same authors
https://news.ycombinator.com/item?id=20019355
This Twitter thread also has a lot of good stuff.
https://twitter.com/maite_taboada/status/1086415051127308288
https://web.archive.org/web/20190527041657/http://people.csa...
Otherwise it's kind of like, I have invented SkyNet in my garage but I am only using it to become richer through the stock market.
It's admirable that you are working on saving human lives. But are human lives actually saved?
From the parent:
> This work has greatly increased accuracy in diagnosis, saving lives.
We don't know if that's a good thing yet.