I read the book Complications by Atul Gawande a while back and it touched on this issue. He mentioned how a computer was more accurate at detecting heart attacks than an experience doctor. He also talked about how if the computer is better than the doctor at reading things like these, it really doesn't make much sense for the doctor to have to evaluate/approve the results. Kind of reminds me of James Simons' thought process on quantitative trading at RenTech.
Sorry about the lack of technical knowledge into the CS stuff, first post here and I haven't really put in the time to learn about CS and AI yet.
> Sorry about the lack of technical knowledge into the CS stuff, first post here and I haven't really put in the time to learn about CS and AI yet.
No need to apologize, you made an interesting and relevant comment. The fact that you feel the need to apologize is more telling of the culture you perceive in this site than of your lack of knowledge in any area.
Hey, thanks for liking the article. I was just looking at my blog stats and they had a bit of spike when you wrote this.
I'm just getting down to writing some posts about the big question the New Yorker article introduces, but doesn't really make any real progress with: will machines actually replace doctors.
A few decades from now, 30 - 50 years at most, doctors and patients, will look back at us and wonder, why we were not using algorithms to assist with diagnosis already.
As with many things - fear that the AI will be wrong (no matter how less frequently this happens than with doctors) and a desire for a human to blame if there is a mistake. If a doctor makes a misdiagnosis and kills the patient as a result it is less bad than if an AI makes a misdiagnosis and kills the patient as a result. It's the one thing I don't understand about most of society.
Same with self driving cars. Self driving cars could be proven to be 100,000% safer than human drivers - but until it is legally mandated people will prefer humans behind the wheel because "what if the self driving car runs a red light and kills someone?" ignoring the hundreds and thousands of humans who run red lights and kill people.
>why we were not using algorithms to assist with diagnosis already.
On the bright side - we increasingly are! I think it's more an issue with budgeting and legal issues that it isn't as widespread.
If you take surgeons, they are trained for one thing. I know a bariatric surgeon who is an expert in medical weight loss but he could not define the word ketogenic. He told me I was foolish for going on a high fat diet (despite the fact that he's overweight).
Also my oncologist didn't know if some vitamins could actually help my cancer and he was skeptical that my diet could effect my IGF1 levels. He couldn't tell me how I got cancer and couldn't tell me any way to prevent it.
It makes me think there's a future potential problem of Establishment AIs vs contrarian AIs? How would it determine what is best with contradictory information?
My research thesis is in this area, hoping for a nice career doing modelling to predict illness. I'd like to have a nice paying job doing something that actually help people instead of just only making somebody more richer.
Also it doesn't matter if the AI gets wrong, the algorithm gives say 80% accuracy and it tells you base on your genetic make up if you should take the surgery route or chemotherapy route.
1. It's to assist the doctor. Also perhaps it can be cheaper to diagnose than a doctor. If say the algorithm says with 80% chance you have cancer, then you should go to your doctor and have it check. If it says no, then don't go. You have another tool to evaluate your health keeping in mind that it's a tool and aid, not a replacement for doctor.
The only concerns are genetic discrimination which GINA law addresses. And medical algorithm usually err on the side of false positive. So it rather get it wrong in saying you have cancer than saying you don't have cancer and in reality you do.
Any body know of any companies that does this please send them my way. ^__^
I am doing this, full-time for almost three years soon. The company is at http://dochuddle.com/
So here is the warning: Medical startups are hard, really hard.
- It is difficult to apply research on 32x32 cat icons on 15megapixel xrays.
- It is difficult to get data without year-long contract efforts
- It is very difficult/expensive, if not impossible, to use cloud resources due to HIPAA and localization rules so you need to build your own on-premise GPU grids like we did
- It is difficult doing most medical things in the USA unless you are on the revenue side (e.g., collections, increasing yield, etc.) -- we got so many raw deal partnership offers in the US that we went overseas to trail our product.
The entire medical system in the US is corrupt from the ground up, geared to maximize revenue with minimal lawsuit risk. Patient care rarely enters the conversation internally. I thought financial services was bad (my past career), but at least the metrics were all agreed upon by everyone. In medicine, everyone has an agenda, often diametrically opposed to other parties.
if you give a flying (sic) about solving medical issues, and actually know a thing or 7 about curing disease, check out Kaggle's competitions. The cervical cancer competition is of particular interest.
If you happen to disagree, step away from your doubt for a sec and listen: we can, and will cure these diseases with AI. That's the whole point. We imbue our intelligence into a machine and voila, the machine does what we ask it to do with greater expediency and more acumen than an individual can do alone. We don't garden with machines and say" wow, this took fifteen thousand people to build, should we use it so we can do other stuff instead?".
Nope. We say, thank you John Deere, I'll take 2. While we are at it, let's look a little deeper and think about how civilization functions in general. Is that not what we do? We connect, we decide to work together, and next thing you know, we improve our quality of life: otherwise known as a corporation (or conglomerate if you wannanother version, ya heard?).
So, is AI good for medicine, yes: it is.
Here's a free thought to prove my point. Using my intellect today I deduced that anxiety is absurdity masked as truth. Imbue that into some AI, you'll heal the minds of the world, ok?
Peas in the ground, head in the air, thoughts with the 1.
-Love
I think it's really useful to see the MD's view of such changes here: https://www.reddit.com/r/medicine/comments/61sgfw/ai_versus_.... Obviously not necessarily representative of how all MDs think but it's interesting to see some fears/concerns they have.
Automating diagnosis goes too far. Deep learning will largely be used for decision support; i.e. surfacing and indicating possible diagnoses for a medical professional to choose from.
Most machine-learning companies have misunderstood the role of radiologists. Their first job is not diagnosis. It is to find the right domain, the right view of the data, from which to formulate a diagnosis. So the problem is to map from one set of parameters, say, how an X-ray is taken, to another more promising set of parameters for the same X-ray, to get the view they need. It's not just see an image, find a tumor.
Kaggle has a few datasets, including I believe mammographs and proteomes if you want to do breast cancer specifically.
If you're not in it for the money, or if just fame is enough, I'd suggest to go upriver: individual diagnostic data is rare because of medical privacy and market forces, but there is boundless open data in, for example genomics and proteomics. If you search for [bioinformatics competition], you should find a nice selection of opportunities with good data availability and clearly defined objectives. ML is slowly revolutionising this field, although it's a good idea to pay attention to what happened before–there were some seriously smart people working on these problems for a long time, and they found some rather ways to extract the most value from data with the tools available at the time.
I recently found http://www.cancerimagingarchive.net/ which actually has some reasonably large collections. There's a public 1400 person breast cancer dataset, and instructions for how to apply for access to the 26254 person National Lung Screening Trial dataset.
[+] [-] sosa2k|9 years ago|reply
Sorry about the lack of technical knowledge into the CS stuff, first post here and I haven't really put in the time to learn about CS and AI yet.
[+] [-] dr_zoidberg|9 years ago|reply
No need to apologize, you made an interesting and relevant comment. The fact that you feel the need to apologize is more telling of the culture you perceive in this site than of your lack of knowledge in any area.
[+] [-] rrherr|9 years ago|reply
https://lukeoakdenrayner.wordpress.com/2016/11/27/do-compute...
[+] [-] lukeor|9 years ago|reply
I'm just getting down to writing some posts about the big question the New Yorker article introduces, but doesn't really make any real progress with: will machines actually replace doctors.
Hope you enjoy them :)
[+] [-] ppod|9 years ago|reply
I think the overall tone of the article is a bit harsh on the machines, especially considering the coda.
[+] [-] mhb|9 years ago|reply
[+] [-] shiven|9 years ago|reply
[+] [-] Nadya|9 years ago|reply
Same with self driving cars. Self driving cars could be proven to be 100,000% safer than human drivers - but until it is legally mandated people will prefer humans behind the wheel because "what if the self driving car runs a red light and kills someone?" ignoring the hundreds and thousands of humans who run red lights and kill people.
>why we were not using algorithms to assist with diagnosis already.
On the bright side - we increasingly are! I think it's more an issue with budgeting and legal issues that it isn't as widespread.
[+] [-] drmindmachine|9 years ago|reply
[+] [-] notfour|9 years ago|reply
Also my oncologist didn't know if some vitamins could actually help my cancer and he was skeptical that my diet could effect my IGF1 levels. He couldn't tell me how I got cancer and couldn't tell me any way to prevent it.
It makes me think there's a future potential problem of Establishment AIs vs contrarian AIs? How would it determine what is best with contradictory information?
[+] [-] digitalzombie|9 years ago|reply
My research thesis is in this area, hoping for a nice career doing modelling to predict illness. I'd like to have a nice paying job doing something that actually help people instead of just only making somebody more richer.
Also it doesn't matter if the AI gets wrong, the algorithm gives say 80% accuracy and it tells you base on your genetic make up if you should take the surgery route or chemotherapy route.
1. It's to assist the doctor. Also perhaps it can be cheaper to diagnose than a doctor. If say the algorithm says with 80% chance you have cancer, then you should go to your doctor and have it check. If it says no, then don't go. You have another tool to evaluate your health keeping in mind that it's a tool and aid, not a replacement for doctor.
The only concerns are genetic discrimination which GINA law addresses. And medical algorithm usually err on the side of false positive. So it rather get it wrong in saying you have cancer than saying you don't have cancer and in reality you do.
Any body know of any companies that does this please send them my way. ^__^
[+] [-] TuringNYC|9 years ago|reply
So here is the warning: Medical startups are hard, really hard. - It is difficult to apply research on 32x32 cat icons on 15megapixel xrays. - It is difficult to get data without year-long contract efforts - It is very difficult/expensive, if not impossible, to use cloud resources due to HIPAA and localization rules so you need to build your own on-premise GPU grids like we did - It is difficult doing most medical things in the USA unless you are on the revenue side (e.g., collections, increasing yield, etc.) -- we got so many raw deal partnership offers in the US that we went overseas to trail our product.
The entire medical system in the US is corrupt from the ground up, geared to maximize revenue with minimal lawsuit risk. Patient care rarely enters the conversation internally. I thought financial services was bad (my past career), but at least the metrics were all agreed upon by everyone. In medicine, everyone has an agenda, often diametrically opposed to other parties.
[+] [-] JusticeJuice|9 years ago|reply
For my thesis I'm trying to design better medical software. Good luck with yours!
[+] [-] StJuice|9 years ago|reply
If you happen to disagree, step away from your doubt for a sec and listen: we can, and will cure these diseases with AI. That's the whole point. We imbue our intelligence into a machine and voila, the machine does what we ask it to do with greater expediency and more acumen than an individual can do alone. We don't garden with machines and say" wow, this took fifteen thousand people to build, should we use it so we can do other stuff instead?".
Nope. We say, thank you John Deere, I'll take 2. While we are at it, let's look a little deeper and think about how civilization functions in general. Is that not what we do? We connect, we decide to work together, and next thing you know, we improve our quality of life: otherwise known as a corporation (or conglomerate if you wannanother version, ya heard?).
So, is AI good for medicine, yes: it is.
Here's a free thought to prove my point. Using my intellect today I deduced that anxiety is absurdity masked as truth. Imbue that into some AI, you'll heal the minds of the world, ok?
Peas in the ground, head in the air, thoughts with the 1. -Love
[+] [-] dhruvp|9 years ago|reply
[+] [-] vonnik|9 years ago|reply
Most machine-learning companies have misunderstood the role of radiologists. Their first job is not diagnosis. It is to find the right domain, the right view of the data, from which to formulate a diagnosis. So the problem is to map from one set of parameters, say, how an X-ray is taken, to another more promising set of parameters for the same X-ray, to get the view they need. It's not just see an image, find a tumor.
[+] [-] Vkkan2016|9 years ago|reply
[+] [-] amelius|9 years ago|reply
[+] [-] matt4077|9 years ago|reply
If you're not in it for the money, or if just fame is enough, I'd suggest to go upriver: individual diagnostic data is rare because of medical privacy and market forces, but there is boundless open data in, for example genomics and proteomics. If you search for [bioinformatics competition], you should find a nice selection of opportunities with good data availability and clearly defined objectives. ML is slowly revolutionising this field, although it's a good idea to pay attention to what happened before–there were some seriously smart people working on these problems for a long time, and they found some rather ways to extract the most value from data with the tools available at the time.
[+] [-] nl|9 years ago|reply
[+] [-] cowardlydragon|9 years ago|reply
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[+] [-] bike4beer|9 years ago|reply
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