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Medicine's Machine Learning Problem

bostonreview.net

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Re: Medicine's Machine Learning Problem

#41

Post by YeGoblynQueenne »

sungam wrote: post #22
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 dermatologist, AI researcher and co-founder of an AI startup (skinsmart.ai) and I would agree with you regarding the utility of AI in making an accurate diagnosis of melanoma. I don't think it has a significant role it play in the Dermatology clinic for this application. However, I am very optimistic about the potential for AI to help in the triage of patients referred to dermatology by non-specialists. For th…

Is it necessary to go so far as making a diagnosis at all? Wouldn't it suffice to detect -and alert the user- that some of her moles have changed shape and she might need to have them looked at more carefully by an expert? This is a task that is very difficult to perform with the naked eye, especially for people with skin types that have lots of moles and an automated decision that could be relied on to detect otherwise imperceptible changes, could perhaps even save some lives.

Re: Medicine's Machine Learning Problem

#42

Post by HPsquared »

DebtDeflation wrote: post #36
refurb wrote: post #29

Earlier quoted context omitted.

First, it took me a very long time to really, fully get that AI is not fundamentally different from a simple linear regression (infering a rule from data). I had a similar revelation. I sat through an AI for health presentation and basically asked “ok, so you take a data set then try and find a set of rules that accurately describes...like a linear regression?” As you said, it’s more sophisticated than that, but in e…

A big part of the problem is the names we've chosen - Artificial Intelligence and Machine Learning. A more accurate, though less sexy, name would have been "Mathematical Pattern Recognition". We can do amazing things with classifiers but we shouldn't fool ourselves into thinking it represents "intelligence".

Brains and intelligence are pretty much just pattern recognition as well - "neurons that fire together wire together"

Re: Medicine's Machine Learning Problem

#43

Post by qayxc »

DebtDeflation wrote: post #36
refurb wrote: post #29

Earlier quoted context omitted.

First, it took me a very long time to really, fully get that AI is not fundamentally different from a simple linear regression (infering a rule from data). I had a similar revelation. I sat through an AI for health presentation and basically asked “ok, so you take a data set then try and find a set of rules that accurately describes...like a linear regression?” As you said, it’s more sophisticated than that, but in e…

A big part of the problem is the names we've chosen - Artificial Intelligence and Machine Learning. A more accurate, though less sexy, name would have been "Mathematical Pattern Recognition". We can do amazing things with classifiers but we shouldn't fool ourselves into thinking it represents "intelligence".

IMHO what's still lacking is the feedback loop.

Current systems are limited to ingesting input and providing output.

A big part of medical diagnosis, however, is to do follow-up exploration based on results of a previous examination.

This is a big part of "intelligence" that's still missing entirely from all approaches that I'm aware of, i.e. the ability to ask further questions/request data based on preliminary results from previous inputs.

Re: Medicine's Machine Learning Problem

#44

Post by qayxc »

HPsquared wrote: post #42
DebtDeflation wrote: post #36

Earlier quoted context omitted.

A big part of the problem is the names we've chosen - Artificial Intelligence and Machine Learning. A more accurate, though less sexy, name would have been "Mathematical Pattern Recognition". We can do amazing things with classifiers but we shouldn't fool ourselves into thinking it represents "intelligence".

Brains and intelligence are pretty much just pattern recognition as well - "neurons that fire together wire together"

Brains in isolation, yes.

But intelligence isn't just a "brain in a jar" situation. Intelligence requires interaction with the environment - you'd never be able to tell whether a Boltzmann Brain is intelligent from just observing it, for example.

Re: Medicine's Machine Learning Problem

#45

Post by krcz »

rscho wrote: post #34
krcz wrote: post #23

Earlier quoted context omitted.

While I don't think AI should replace humans in describing medical images, it can be used to check if they might have missed something. Such AI-based description should be provided only after the human finishes analyzing the image, to avoid lazy technicians just copying algorithmic output. The goal doesn't have to be increasing accuracy and not doing biopsies, it might be reducing number of false negatives.

Then technicians will just put whatever diagnosis in the relevant text field and let the "AI" do their job (if the "AI" is deemed good enough). I've been working in healthcare for 15 years, and I don't have a single doubt that that's what would happen. Conversely, if the "AI" is deemed not good enough, it will be business as usual and nobody will so much as glance at the "AI" results.

My idea was:

1. Technician writes down their diagnosis

2. They submit it to the system

3. AI comes with its own analysis

4. Technician sees the outcome, they can update their assessment

5. Everything is saved into the system

If one of technicians has too much errors in their initial assessments, it should raise a concern.

Re: Medicine's Machine Learning Problem

#46

Post by sungam »

YeGoblynQueenne wrote: post #41
sungam wrote: post #22

Earlier quoted context omitted.

I am a dermatologist, AI researcher and co-founder of an AI startup (skinsmart.ai) and I would agree with you regarding the utility of AI in making an accurate diagnosis of melanoma. I don't think it has a significant role it play in the Dermatology clinic for this application. However, I am very optimistic about the potential for AI to help in the triage of patients referred to dermatology by non-specialists. For th…

Is it necessary to go so far as making a diagnosis at all? Wouldn't it suffice to detect -and alert the user- that some of her moles have changed shape and she might need to have them looked at more carefully by an expert? This is a task that is very difficult to perform with the naked eye, especially for people with skin types that have lots of moles and an automated decision that could be relied on to detect otherw…

Yes this is the idea of mole mapping and there are 3D whole body photo imaging systems available for this with automated detection of changing lesions. It's harder to do on a smart phone but maybe possible.

Re: Medicine's Machine Learning Problem

#47

Post by YeGoblynQueenne »

jjcon wrote: post #11
aisofteng wrote: post #9

Earlier quoted context omitted.

As a fellow practitioner, I entirely agree. Actually, reading this article made something click for me regarding the oft discussed and denigrated “bias in AI” always brought up in discussions of the “ethics of AI”: there is no bias problem in the algorithms of AI. AI algorithms _need_ bias to work. This is the bias-variance trade off: https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff The problem is having the _c…

> AI algorithms _need_ bias to work. This is the bias-variance trade off: https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff To be clear, statistical bias is in fact distinct from the colloquial term ‘bias’ most people use - but they can be interpreted similarly if given the proper context (which you did)

In machine learning the "bias" that relates to the bias-variance tradeoff is inductive bias, i.e. the bias that a learning system has in selecting one generalisation over another. A good quick introduction to that concept is in the following article:

Why We Need Bias in Machine Learning Algorithms

https://towardsdatascience.com/why-we-need-bias-in-machine-l...

The article is a simplified discussion of an early influential paper on the need for bias in machine learning by Tom Mitchell:

The need for bias in learning generalizations

http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/Need%20fo...

The "dataset bias" that you and the other poster are discussing is better described in terms of sampling error: when sampling data for a training dataset, we are sampling from an unknown real distribution and our sampling distribution has some error with respect to the real one. This error manifests as generalisation error (with respect to real-world data, rather than a held-out test set), because the learning system learns the distribution of its training sample. Unfortunately this kind of error is difficult to measure and is masked by the powerful modelling abilities of systems like deep neural networks, who are very capable at modelling their training distribution (and whose accuracy is typically measured on a held-out test set, sampled with the same error as the rest of the training sample). It is this kind of statistical error that is the subject of articles discussing "bias in machine learning".

Inductive bias has nothing to do with such "dataset bias and is in fact independent from dataset bias. Rather, inductive bias is a property of the learning system (e.g. a neural net architecture). Consequently, it is not possible to "eliminate" inductive bias - machine learning is impossible without it! The two should absolutely not be confused, they are not similar in any context and should not be interpreted as in any way similar.

Re: Medicine's Machine Learning Problem

#48

Post by HPsquared »

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.

Re: Medicine's Machine Learning Problem

#49

Post by sjg007 »

sungam wrote: post #39
sjg007 wrote: post #37

Earlier quoted context omitted.

If I had a benign lesion referred by my PCP to dermatology, I’d want a dermatologist to take a look at it. It’s never been difficult to get a dermatology appointment.

Situation may be slightly different in the NHS (national health service) where there is an overwhelming number of referrals from general practitioners for suspected skin cancer most of which turn out to be benign. As a consequence there is lack of capacity to see patients with other skin conditions. Of course it's always possible to see a private Dermatologist if you have health insurance or are happy to pay.

If that’s true that sounds like a different problem. Maybe they need to train more dermatologists? And if there are appointments available privately well... I don’t know what to say. Seems like an structural systemic failure which is odd. Maybe dermatologists are gaming the system to induce private pay..

Re: Medicine's Machine Learning Problem

#50

Post by [deleted] »

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.

[deleted]
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