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Medicine's Machine Learning Problem

bostonreview.net

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Re: Medicine's Machine Learning Problem

#91

Post by BlueTemplar »

HPsquared wrote: post #48
DoreenMichele wrote: post #19

"The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency." -- Bill Gates In the lifetime of my adult sons the world stopped being predominantly agrarian and rural and hit a point where more than half of all people on the planet live in cities. It…

It's a lot like the emergence of scientific forestry as described in "Seeing Like a State" - instead of local knowledge and care/attention to individual circumstances by a generalist, the field has become standardised and based around things which can be easily measured.

Exactly, the article blames ML, but these issues of concentration of power and of knowledge are at least as old as State-istics !

Re: Medicine's Machine Learning Problem

#93

Post by BlueTemplar »

n3ur0n wrote: post #52
gdebel wrote: post #14

I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest. After 3 years of research, playing with datasets, extracting and cleaning data from EMR and from different machines, I not sure that the biggest problem with the so-called "AI" is the inequalities that it can induce ; it is rather, is it useful at all ? This is a little b…

I do respect your experience and take on the matter, however, let's replace this statement: "I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest." with: I'm a [machine learning researcher] and self-taught [ophthalmologist], I started to learn [ophthalmology] in 2016 when the [clinical medicine] hype was at his highest. In t…

I have barely any biology/medecine nor Machine Learning knowledge (though some physics, maths, programming), yet I might have to do an internship in the field of ML applied to leukocyte classification, where would you recommend to start ?

Re: Medicine's Machine Learning Problem

#94

Post by plumeria »

gdebel wrote: post #70
blueblisters wrote: post #16

Earlier quoted context omitted.

This is an interesting perspective. Since you're an eye surgeon, this might be a relevant question. What do you think of the relative success of Diabetic Retinopathy (DR) diagnostic models, especially the FDA approval of the clinical trials Digital Diagnostics (formerly IDxDR) [1]? Their approach to the model architecture was slightly different from the black-box approach of other labs, wherein IDxDR's model is train…

Honestly, I don't know what to think. Ophthalmology is a great field for AI researchers (lots of images: the eye is an organ that you can photograph and analyze visually in every angles, almost like in dermatology). In Ophthalmology, diabetic retinopathy is an evident take : lots of people involved, lots of annotated pictures available, screening programs. However, I would like to see the performances of the algorith…

Do you happen to know of a dataset that contains a fundus picture of the back of the eye plus a regular high-res photo of the full eye?

Re: Medicine's Machine Learning Problem

#95

Post by Gatsky »

gdebel wrote: post #14

I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest. After 3 years of research, playing with datasets, extracting and cleaning data from EMR and from different machines, I not sure that the biggest problem with the so-called "AI" is the inequalities that it can induce ; it is rather, is it useful at all ? This is a little b…

I am a medical oncologist in a similar position to you. I agree completely. There is a lot of hype over 'AI' and big data, and plainly ridiculous statements about replacing doctors etc.

The technology is fascinating, and there has been a lot of interesting and clever innovations. But the value add for healthcare is pretty small in the end. The value add for the actual patient is even more miniscule. There is some misunderstanding about what doctors actually do and what patients want doctors to do. Fundamentally, a patient wants to see a doctor that is going to take responsibility for their medical problem, and ideally their health. When this is achieved, the outcomes are usually optimal under constraints, and the clinician and patient are both satisfied. 'AI' as it is known today could never do this. Even if someone unveils superhuman general AI tomorrow, it wouldn't be able to do this.

There are various niche applications of machine learning which employ interesting technology in image interpretation, and this will be a useful addition to the suite of tools already present, particularly where non-specialists need to look at a picture or radiological image, and for screening.

But otherwise I expect that in 20 years time we will still be saying that EMR data is too difficult to deal with, and uptake of machine learning based software will be very low. We still don't have useful automated reporting of electrocardiograms, for example. So even sophisticated curve fitting seems to have trouble fitting these literal curves in a way that impacts clinical care.

Re: Medicine's Machine Learning Problem

#96

Post by tpoacher »

gdebel wrote: post #14

I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest. After 3 years of research, playing with datasets, extracting and cleaning data from EMR and from different machines, I not sure that the biggest problem with the so-called "AI" is the inequalities that it can induce ; it is rather, is it useful at all ? This is a little b…

Honest question. If a Datascientist with 20 years experience said "I also dabbled with eye surgery for 3 years" and their authoritative conclusion was that modern eye surgery is effectively trivial. How would you respond to that?

Re: Medicine's Machine Learning Problem

#97

Post by 908B64B197 »

dontreact wrote: post #3

It's a hard problem to work around which is rooted in the data available. I published this paper while I was at Google: https://www.nature.com/articles/s41591-019-0447-x The only data we were able to get at the time was mostly white patients. We talked to many hospitals but many were/are reluctant to share anonymized data for research. I'm not at Google so I'm not sure the status of the project now, but there was a r…

> The only data we were able to get at the time was mostly white patients. We talked to many hospitals but many were/are reluctant to share anonymized data for research

I joked to someone from a small country with public healthcare that the best thing they could do was release as much anonymized high quality data and essentially get every machine learning algorithm tuned for their population for free.

It's like adding your code to a popular CPU benchmark.

Re: Medicine's Machine Learning Problem

#98

Post by 908B64B197 »

DoreenMichele wrote: post #19

"The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency." -- Bill Gates In the lifetime of my adult sons the world stopped being predominantly agrarian and rural and hit a point where more than half of all people on the planet live in cities. It…

> Human doctors were historically put on call for up to 24 hours at a time as part of their learning process in part so they would see a patient's condition evolve over time while the doctor was still young and healthy enough to endure this grueling process. Having seen it for a time as part of their training, they retained that knowledge when they were older and could recognize a stage of a moving target.

Isn't it more because, unlike most other fields, the medical field systematically refuses to learn best practices to hand-over information? So they prefer to have a heavily sleep-deprived resident they can abuse rather than a better way to document and to pass information.

Re: Medicine's Machine Learning Problem

#99

Post by 908B64B197 »

gdebel wrote: post #14

I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest. After 3 years of research, playing with datasets, extracting and cleaning data from EMR and from different machines, I not sure that the biggest problem with the so-called "AI" is the inequalities that it can induce ; it is rather, is it useful at all ? This is a little b…

> I also realized that it is extremely difficult, if not impossible, to use data from EMR out of the box. Medical data is dirty, intrinsically, because humans are complex things that do not fit easily in little boxes. Hence, if you want quality data, you have to think your data collection in advance, and motivate all your fellow doctors to check the little boxes correctly. For many years (we are talking about big data, no ?) Of course, there is some exceptions, but most of the time the data cleaning process is extremely hard to perform (however, if a dedicated team of people with medical knowledge is concentrated on this work, things could be different. I had to clean the data myself).

Paper charts everywhere. Post it in some files.

Wonder how much ML could help with mammogram. If you ever read one of these, you mostly use symmetry and check the previous ones to spot what changed.

Re: Medicine's Machine Learning Problem

#100

Post by tpfour »

gdebel wrote: post #14

I'm an eye surgeon and self-taught machine learning practitioner, I started to learn Python in 2016 when the deep learning hype was at his highest. After 3 years of research, playing with datasets, extracting and cleaning data from EMR and from different machines, I not sure that the biggest problem with the so-called "AI" is the inequalities that it can induce ; it is rather, is it useful at all ? This is a little b…

Tangential: I've always had an interest for medicine. A decade or so into software dev and I'm not convinced I want to stay. I've heard a lot of doctors say they wouldn't do it all over again. I'm wondering if med school makes any sense. Maybe my idea of medicine is too influenced by Hollywood, but it does look very interesting. Would you study medicine again given the chance to start over?
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