13 ms·

Medicine's Machine Learning Problem

jjcon 6y ago
Non clickbait headline: Medicine has some data collection biases (if your aim is to represent US demographics). Long existing non-ML methods suffer due to this data collection bias but again for some reason the author seems to put AI in a special mysterious place on a pedestal. They use innuendo and anecdote to make assertions without evidence to back them up as systemic problems unique to AI. Innovation will never happen if new technology has to perform perfectly, it only has to perform better than existing methods.
- BadInformatics 6y ago
  I'm of two minds about this article. It does a reasonable job of enumerating the issues with naively deploying ML in a healthcare setting. However, these articles are becoming a dime a dozen and there is little actionable talk on how to discover or mitigate these issues at a level that practitioners can use. To your point about the bar for new tech, I agree that singling out AI/ML is a cheap shot and more speculative FUD without concrete evidence. That said, we have seen no shortage of hucksters and self-aggrandizing members of the "move fast and break things crowd" trying to treat medicine as a beginner-level Kaggle challenge. This has become particularly egregious and noticeable during the pandemic [1]. The respective lack of medical and technical literacy among programmers/"data scientists" and healthcare providers/admins is just more fuel for the fire. [1] https://www.reddit.com/r/MachineLearning/comments/fni5ow/d_why_is_the_ai_hype_absolutely_bonkers/ https://www.reddit.com/r/MachineLearning/comments/fni5ow/d_w...
dontreact 6y ago
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 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 real attempt to try and gather more diverse data. Unfortunately there were a lot of obstacles put up by those who have the data (hospital systems). Fundamentally, it seems to me like there just aren't as many lung cancer screening scans out there for non-white patients as there are for white patients. How do we get around this? How do we improve on the situation? I fundamentally believe that machine learning in the long term can make medicine more accessible to more diverse groups, but not if we shoot it down out of fearmongering right away. I agree that bias is a problem, but part of what needs to happen to get more diverse data is simply having more data available. There is real promise in this technology and if we have a one dimensional view of it ("is it or is it not dangerous because of bias/privacy") then we will fail to get past the initial humps related to fear and distrust.
- aisofteng 6y ago
  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 https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff The problem is having the _correct_ bias. If there are physiological differences in a disease between men and women and you have a good dataset, the bias in that dataset is the bias of “people with this disease”. If there is no such well-balanced dataset, what is being revealed is a pre-existing harmful bias in the medicinal field of sample bias in studies. If anything, we should be thankful that the algorithms used in AI, based on statistical theory that has carefully been developed over decades to be objective, is revealing these problems in the datasets we have been using to frame our understanding of real issues. Next up, the hard part: eliminating our dataset biases and letting statistical learning theory and friends do what they have been designed to do and can do well.
  - jjcon 6y ago
    > AI algorithms _need_ bias to work. This is the bias-variance trade off: https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff 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)
    - YeGoblynQueenne 6y ago
      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-learning-algorithms-eff0343174c0 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%20for%20Bias.pdf 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.
daginnbotty 6y ago
Question about this: they talk about how datasets are not representative, my question is, compared to what? I’m guessing by the politic of this article the person is from the US, do they want people to look at data representative of US population? That seems pretty narrow-minded. My country has almost no black people (probably <1% though I don’t know that official stats on race are even collected) and it would be extremely expensive for us to include black people at US levels (13%) in all research. Perhaps they are advocating we use world demographics but that would be logistically impossible for basically all researchers around the world. Am I misunderstanding the author or does the article seem pretty ethnocentric? (is that the right word?) US-centric? Wouldn’t it be better to just qualify/label the demographics of the research data (we used all black people, all white people etc). They talk as if there is some golden ratio we should all be following but that just isn’t the case. In any case, I don’t think it is useful to shame researchers that are just doing the best they can with the data available cause the data they have is useful to someone.
- BadInformatics 6y ago
  For better or worse, most of these articles are US-centric because that's where most of the R&D money for health ML is. The far more reasonable approach is to make sure your data contains as many demographics as possible (not just race) from your actual patient population. If there happen to be gaps, then put in at least a reasonable effort to fill them instead of shrugging and saying "it's out of our control". That, along with a per-demographic breakdown of important metrics and your point about qualifying demographics in the data (which is already done in most medical publications, including many using ML) would already be a huge improvement on what most people do now. Ironically, it's the big tech companies that have the hardest time with this because they want to make generally deployable projects, yet don't have access to as much data as many healthcare orgs do. Frankly, I don't have much sympathy for them: a lot of this is trust issues from self-inflicted damage.
  - jjcon 6y ago
    > make sure your data contains as many demographics as possible (not just race) from your actual patient population By this you mean researchers should mirror their patient population as closely as possible (be it socioeconomic, gender, race etc) in whatever region they may operate in (which may not scale perfectly worldwide but will serve patients in that region well)?
worik 6y ago
One problem is mistaking statistical analysis for learning, data for knowledge
- visarga 6y ago
  It's only a problem if it doesn't work. I'm happy with imperfect protein folders that beat SOTA by 100% and with DALL.E drawing the radish-in-tutu-walking-a-dog and a harp-snail on request. I'll be happy also with the slightly unexplainable medical diagnosis that still beats the average expert in that field. And getting good unbiased data for these algorithms is going to happen eventually.
nitwit005 6y ago
Feels like a rant about health care bias that was updated to include some mention of ML, but is still mostly about issues that existed previously.
gdebel 6y ago
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 bit provocative so let me explain. 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). More powerful, but definitely, no intelligence added. Most of my fellow doctor colleagues still think that AI is special, different, like magic; I also thought like this before learning to code. Scores inferred from data were used in medicine from decades and fundamentally, nothing changed with the AI wave. I'm extremely impressed with the performances of GAN for image generation, and with what allows deep RL in controlled environments (which the human body is not): however, I can't see any applications of those technologies in medicine. Ok, Deep Learning allows to analyze images with a great level of performances. However, at the end of the day, nobody (informed) wants an AI diagnosis and the doctor will read the images. He will maybe have a pre-completed report : wow, incredible. We are very far from the disappearance of radiologists that Geoffrey Hinton took for granted a few years ago. At this time, a team published in Nature a paper about a DL algorithm which could diagnose melanomas better than dermatologists, using a picture. Unfortunately, no real-life application. Why? Because when you suspect a melanoma, if you have any doubt, you won't take a chance: you will take a biopsy. What is the interest of guessing the result of the biopsy that you will do anyway, because if you guessed wrong, the patient dies? No interest. 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). I'll finish with the most ironic part : I dedicated a few years of my life to a topic where both optics and prediction from data are involved (intraocular lens calculation in cataract surgery). I tried a great deal of ML approaches, only to find recently that by better accounting for the optical specificities of the problem I was trying to solve, I obtained excellent results, better than with ML, even with a dumb multiple regression. Ouch. The lesson is : physics beats AI. I would be happy to be challenged on this topic.
DoreenMichele 6y ago
"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 wasn't hugely long ago -- a hundred or two hundred years -- that most people lived in little villages or tribes and didn't travel all the far and knew most people they dealt with. The local doctor -- or medicine man -- was often one of the older, best educated and wisest locals. He tended to come to your home with a little black bag and in the course of walking through your house to check on you if you were ailing and not able to get out of bed, he saw a great many things about your life without having to ask. This informed his conclusions about what was wrong and about how to treat it. And it did so in a way that was largely invisible to the recipients of care. Doctors likely often didn't explain that the house was filthy or the spouse was obviously abusive. Topics like that tend to be socially unacceptable and people don't like being criticized in that way, but if someone smarter and more experienced and better educated and wiser walks through your life and then prescribes something "for your health" and he has a track record of fixing the problem, you do as you are told because you were told it. And then modern medicine invented a lot of diagnostics and what not and office visits by the patient replaced home visits because we haven't invented a Tricorder that can replace a little black bag and let you bring all that diagnostic power with you. Human health is no longer treated like the logical outcome of all your life choices and your physician is no longer the wisest person you know giving you good advice that takes into account a great many factors you never talked about with him. People get treated like specimens in a petri dish in a way that implicitly denies the fact that their physical state of health is the sum total of all their life choices. In tribal cultures, medicine men were typically people who tended to both spiritual and physical health. The two were not viewed as separate from each other. Medicine has become commercialized in a way that doesn't really serve the interests of the patient and if you try to point that out you are likely to be written off as some paranoid fruitcake and conspiracy theorist. There are a lot of good things about modern medicine, but there are also a lot of systemic issues and this article is correct to point out that AI tends to magnify those sorts of things. Last, health is best understood as a moving target in 4D. Data capture does a poor job of approaching it that way and I'm not aware of any programs that are well equipped to do a good job with that. 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. I don't know how much that is still done, but I don't think we really frame AI in that way. I don't know how we would get there from here either. I still haven't managed to learn to code, what with being too busy with my own health issues all these years.
hobom 6y ago
This includes some true things about data collection issues, but I cannot agree with the main thesis that ML algorithms are about power. If anything, they shift power to the patients because now the decisions can be checked and questioned at many levels. An algorithm will not send you away because it is to tired too take your complaints seriously. So, using algorithmic decision making should be as bias free as possible, but there is no way that across the board they will be more biased than humans are now. If people care about marginalized communities, they should push with everything they got for, not oppose, ML decision making.
rscho 6y ago
The current effort towards medical ai is heading in the wrong direction: were trying to make machines adapt to the field while we should be trying to adapt the field to make it available to machine-aided reasoning. Problem: almost nobody understands both medicine and machines well enough to bridge the abysmal communication gap separating AI/CS and medical professionals.
- vagrantJin 6y ago
  I think your reason is dubious at best and thoroughly impractical. Machines aid our work. We dont work to aid machines. The problems in medicine are hard because biology is hard. I dont think we understand the depth of knowledge we have yet to uncover. Not really. We intuit it but we don't know.
  - rscho 6y ago
    The medical field is currently supported by clinical intuition much more than by hard data, or anything included into the common definition of "science". We should absolutely work to make medical information systems available to machines. Actually, "AI" won't work well until this happens.
BlueTemplar 6y ago
This reminds me how ML forms of AI are indirectly forbidden around here for government use, due to the lack of transparency.

Non clickbait headline: Medicine has some data collection biases (if your aim is to represent US demographics). Long existing non-ML methods suffer due to this data collection bias but again for some reason the author seems to put AI in a special mysterious place on a pedestal. They use innuendo and anecdote to make assertions without evidence to back them up as systemic problems unique to AI. Innovation will never happen if new technology has to perform perfectly, it only has to perform better than existing methods.

BadInformatics 6y ago
I'm of two minds about this article. It does a reasonable job of enumerating the issues with naively deploying ML in a healthcare setting. However, these articles are becoming a dime a dozen and there is little actionable talk on how to discover or mitigate these issues at a level that practitioners can use. To your point about the bar for new tech, I agree that singling out AI/ML is a cheap shot and more speculative FUD without concrete evidence. That said, we have seen no shortage of hucksters and self-aggrandizing members of the "move fast and break things crowd" trying to treat medicine as a beginner-level Kaggle challenge. This has become particularly egregious and noticeable during the pandemic [1]. The respective lack of medical and technical literacy among programmers/"data scientists" and healthcare providers/admins is just more fuel for the fire. [1] https://www.reddit.com/r/MachineLearning/comments/fni5ow/d_why_is_the_ai_hype_absolutely_bonkers/ https://www.reddit.com/r/MachineLearning/comments/fni5ow/d_w...

dontreact 6y ago

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 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 real attempt to try and gather more diverse data. Unfortunately there were a lot of obstacles put up by those who have the data (hospital systems). Fundamentally, it seems to me like there just aren't as many lung cancer screening scans out there for non-white patients as there are for white patients. How do we get around this? How do we improve on the situation? I fundamentally believe that machine learning in the long term can make medicine more accessible to more diverse groups, but not if we shoot it down out of fearmongering right away. I agree that bias is a problem, but part of what needs to happen to get more diverse data is simply having more data available. There is real promise in this technology and if we have a one dimensional view of it ("is it or is it not dangerous because of bias/privacy") then we will fail to get past the initial humps related to fear and distrust.

aisofteng 6y ago
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 https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff The problem is having the _correct_ bias. If there are physiological differences in a disease between men and women and you have a good dataset, the bias in that dataset is the bias of “people with this disease”. If there is no such well-balanced dataset, what is being revealed is a pre-existing harmful bias in the medicinal field of sample bias in studies. If anything, we should be thankful that the algorithms used in AI, based on statistical theory that has carefully been developed over decades to be objective, is revealing these problems in the datasets we have been using to frame our understanding of real issues. Next up, the hard part: eliminating our dataset biases and letting statistical learning theory and friends do what they have been designed to do and can do well.
- jjcon 6y ago
  > AI algorithms _need_ bias to work. This is the bias-variance trade off: https://en.m.wikipedia.org/wiki/Bias–variance_tradeoff 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)
  - YeGoblynQueenne 6y ago
    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-learning-algorithms-eff0343174c0 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%20for%20Bias.pdf 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.

daginnbotty 6y ago

Question about this: they talk about how datasets are not representative, my question is, compared to what? I’m guessing by the politic of this article the person is from the US, do they want people to look at data representative of US population? That seems pretty narrow-minded. My country has almost no black people (probably <1% though I don’t know that official stats on race are even collected) and it would be extremely expensive for us to include black people at US levels (13%) in all research. Perhaps they are advocating we use world demographics but that would be logistically impossible for basically all researchers around the world. Am I misunderstanding the author or does the article seem pretty ethnocentric? (is that the right word?) US-centric? Wouldn’t it be better to just qualify/label the demographics of the research data (we used all black people, all white people etc). They talk as if there is some golden ratio we should all be following but that just isn’t the case. In any case, I don’t think it is useful to shame researchers that are just doing the best they can with the data available cause the data they have is useful to someone.

BadInformatics 6y ago
For better or worse, most of these articles are US-centric because that's where most of the R&D money for health ML is. The far more reasonable approach is to make sure your data contains as many demographics as possible (not just race) from your actual patient population. If there happen to be gaps, then put in at least a reasonable effort to fill them instead of shrugging and saying "it's out of our control". That, along with a per-demographic breakdown of important metrics and your point about qualifying demographics in the data (which is already done in most medical publications, including many using ML) would already be a huge improvement on what most people do now. Ironically, it's the big tech companies that have the hardest time with this because they want to make generally deployable projects, yet don't have access to as much data as many healthcare orgs do. Frankly, I don't have much sympathy for them: a lot of this is trust issues from self-inflicted damage.
- jjcon 6y ago
  > make sure your data contains as many demographics as possible (not just race) from your actual patient population By this you mean researchers should mirror their patient population as closely as possible (be it socioeconomic, gender, race etc) in whatever region they may operate in (which may not scale perfectly worldwide but will serve patients in that region well)?

worik 6y ago

One problem is mistaking statistical analysis for learning, data for knowledge

visarga 6y ago
It's only a problem if it doesn't work. I'm happy with imperfect protein folders that beat SOTA by 100% and with DALL.E drawing the radish-in-tutu-walking-a-dog and a harp-snail on request. I'll be happy also with the slightly unexplainable medical diagnosis that still beats the average expert in that field. And getting good unbiased data for these algorithms is going to happen eventually.

nitwit005 6y ago

Feels like a rant about health care bias that was updated to include some mention of ML, but is still mostly about issues that existed previously.

gdebel 6y ago

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 bit provocative so let me explain. 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). More powerful, but definitely, no intelligence added. Most of my fellow doctor colleagues still think that AI is special, different, like magic; I also thought like this before learning to code. Scores inferred from data were used in medicine from decades and fundamentally, nothing changed with the AI wave. I'm extremely impressed with the performances of GAN for image generation, and with what allows deep RL in controlled environments (which the human body is not): however, I can't see any applications of those technologies in medicine. Ok, Deep Learning allows to analyze images with a great level of performances. However, at the end of the day, nobody (informed) wants an AI diagnosis and the doctor will read the images. He will maybe have a pre-completed report : wow, incredible. We are very far from the disappearance of radiologists that Geoffrey Hinton took for granted a few years ago. At this time, a team published in Nature a paper about a DL algorithm which could diagnose melanomas better than dermatologists, using a picture. Unfortunately, no real-life application. Why? Because when you suspect a melanoma, if you have any doubt, you won't take a chance: you will take a biopsy. What is the interest of guessing the result of the biopsy that you will do anyway, because if you guessed wrong, the patient dies? No interest. 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). I'll finish with the most ironic part : I dedicated a few years of my life to a topic where both optics and prediction from data are involved (intraocular lens calculation in cataract surgery). I tried a great deal of ML approaches, only to find recently that by better accounting for the optical specificities of the problem I was trying to solve, I obtained excellent results, better than with ML, even with a dumb multiple regression. Ouch. The lesson is : physics beats AI. I would be happy to be challenged on this topic.

DoreenMichele 6y ago

"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 wasn't hugely long ago -- a hundred or two hundred years -- that most people lived in little villages or tribes and didn't travel all the far and knew most people they dealt with. The local doctor -- or medicine man -- was often one of the older, best educated and wisest locals. He tended to come to your home with a little black bag and in the course of walking through your house to check on you if you were ailing and not able to get out of bed, he saw a great many things about your life without having to ask. This informed his conclusions about what was wrong and about how to treat it. And it did so in a way that was largely invisible to the recipients of care. Doctors likely often didn't explain that the house was filthy or the spouse was obviously abusive. Topics like that tend to be socially unacceptable and people don't like being criticized in that way, but if someone smarter and more experienced and better educated and wiser walks through your life and then prescribes something "for your health" and he has a track record of fixing the problem, you do as you are told because you were told it. And then modern medicine invented a lot of diagnostics and what not and office visits by the patient replaced home visits because we haven't invented a Tricorder that can replace a little black bag and let you bring all that diagnostic power with you. Human health is no longer treated like the logical outcome of all your life choices and your physician is no longer the wisest person you know giving you good advice that takes into account a great many factors you never talked about with him. People get treated like specimens in a petri dish in a way that implicitly denies the fact that their physical state of health is the sum total of all their life choices. In tribal cultures, medicine men were typically people who tended to both spiritual and physical health. The two were not viewed as separate from each other. Medicine has become commercialized in a way that doesn't really serve the interests of the patient and if you try to point that out you are likely to be written off as some paranoid fruitcake and conspiracy theorist. There are a lot of good things about modern medicine, but there are also a lot of systemic issues and this article is correct to point out that AI tends to magnify those sorts of things. Last, health is best understood as a moving target in 4D. Data capture does a poor job of approaching it that way and I'm not aware of any programs that are well equipped to do a good job with that. 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. I don't know how much that is still done, but I don't think we really frame AI in that way. I don't know how we would get there from here either. I still haven't managed to learn to code, what with being too busy with my own health issues all these years.

hobom 6y ago

This includes some true things about data collection issues, but I cannot agree with the main thesis that ML algorithms are about power. If anything, they shift power to the patients because now the decisions can be checked and questioned at many levels. An algorithm will not send you away because it is to tired too take your complaints seriously. So, using algorithmic decision making should be as bias free as possible, but there is no way that across the board they will be more biased than humans are now. If people care about marginalized communities, they should push with everything they got for, not oppose, ML decision making.

rscho 6y ago

The current effort towards medical ai is heading in the wrong direction: were trying to make machines adapt to the field while we should be trying to adapt the field to make it available to machine-aided reasoning. Problem: almost nobody understands both medicine and machines well enough to bridge the abysmal communication gap separating AI/CS and medical professionals.

vagrantJin 6y ago
I think your reason is dubious at best and thoroughly impractical. Machines aid our work. We dont work to aid machines. The problems in medicine are hard because biology is hard. I dont think we understand the depth of knowledge we have yet to uncover. Not really. We intuit it but we don't know.
- rscho 6y ago
  The medical field is currently supported by clinical intuition much more than by hard data, or anything included into the common definition of "science". We should absolutely work to make medical information systems available to machines. Actually, "AI" won't work well until this happens.

BlueTemplar 6y ago

This reminds me how ML forms of AI are indirectly forbidden around here for government use, due to the lack of transparency.