4 ms·
I'm generally pretty skeptical of claims of applying machine learning to improve healthcare outcomes (they often seem to be a poorly thought-out veneer), but at
by qchris 5y ago
I'm generally pretty skeptical of claims of applying machine learning to improve healthcare outcomes (they often seem to be a poorly thought-out veneer), but at first glance, this one actually seems pretty exciting. It also seems like it's more of a "machines aiding humans, not replacing them" situation which, especially in context of healthcare, is always nice to see.
> Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilizing what's demonstrated in this work to be critical data.
> "The images carry critical information that doctors haven't been able to access," said first author Dan Popescu, a former Johns Hopkins doctoral student.
This is, like, the thing that machine learning is really good at--pulling out additional information from complex patterns represented as imagery. It's a very similar reasoning to why neural networks can be really great at classifying malware based on the program structure or doing semantic segmentation on well-defined scenes.
> The team trained a second neural network to learn from 10 years of standard clinical patient data, 22 factors such as patients' age, weight, race, and prescription drug use.
Again, this kind of multi-dimensional pattern-matching is pretty much much a machine learning wheelhouse.
> [They] were validated in tests with an independent patient cohort from 60 health centers across the United States, with different cardiac histories and different imaging data
And apparently has a bunch of independent validation. Neat stuff, I'm curious to see how it will develop/be distributed in the future.
Disclosure: I recently accepted a job offer from JHU, albeit in a different field.
- emerged 5y agoI’m a bit more cynical, given Technology’s track record in all other industries. Using AI in medical contexts is quite frightening to me. I don’t think we think it through at the pace it moves.
- qchris 5y agoI get the skepticism, truly--I actually wrote a couple articles about ethics and machine learning, and I completely agree there have definitely been cranks or bad actors using this kind of technology in both this space and many others. But if you were going to apply data science/machine learning to healthcare (personally, I dislike the term "AI"), I think that this project would be very close to both how and where you would want it. Peer-reviewed research with independent confirmation, in an narrow, image-based diagnosis, overseen by medical professionals throughout the process (not directly making recommendations to patients), by one of the world's leading medical research institutions. This isn't a catch-all, and it's certainly possible that cynicism could be warranted down the line. But for me, personally, I think that cautious optimism for this specific situation overrides the cynicism.
- vmception 5y ago> It also seems like it's more of a "machines aiding humans, not replacing them" situation which, especially in context of healthcare, is always nice to see. Everything I’ve seen has been about flagging stuff for further human review, what things have you seen that have made you so skeptical?
- hans1729 5y ago[not the gp] the big problem with ML in medicine is that the experts of the two fields lack the - in both cases very deep - domain expertise of the other field. Ask yourself much much top talent in ML there really is, now ask yourself how many of those work on a specific problem or toolset in medicine. Then ask yourself how much top talent in the medical fields has a profound intersection with statistics. Both are deep fields dealing with inherently complicated problem spaces. The amount of people who are able to make profound contributions to both fields can probably fit in a medium-sized conference room, while there are hundreds, if not a magnitude more, ML-driven medical research projects. It's hard enough making contributions to either field without heavily intersecting with a completely different domain, and a lot of the best-in-class experts of ML (or, better: AI (!)) are busy solving things like protein folding in London. And if you look at those guys faces, they look tired, because what they are doing is really, really hard.
- vmception 5y agoI think this is great for all the people that feel invalidated by doctors This could help women that say so, as well as minorities, and even more so for minority women No longer would people’s complaints be dismissed as heuristics (edit: hysterics) just because a doctor doesn't understand how people express themselves at least for predictors of cardiac arrest