12 ms·

This is a really good point and example. I spent 10 years in mammography software, and I saw first hand how many outside factors can impact a physician's decis

This is a really good point and example. I spent 10 years in mammography software, and I saw first hand how many outside factors can impact a physician's decision to biopsy or not a given artifact on an image. Things like family history, patient's history, cycle timing, age, weight, other risk factors all play a role in a smart radiologist making the right decision for that patient. And the pattern recognition on top of that is really hard - it's not just about the pattern you see at a particular spot in an image, it's the whole image in the context of what that looks like. Could ML get better over time with this? Sure...but they've been using CAD in mammography for decades and it still hasn't replaced radiologists at all. Could a model be made to include those over variables? Sure...but again the complexity of that kind of decision making is something that requires a lot more "intelligence" than any AI or ML system exhibits today and in my mind in the foreseeable future. Just collecting that data in a structured, consistent way is more challenging than people realize.

mumblemumble 5y ago
I see the same thing in natural language processing. A lot of important details come from outside the four corners of the document. Ironically, I often find myself in the unenviable position of being the machine learning person who's trying to convince people that machine learning is probably not a good fit for their problem. Or, worse, taking some more fuzzy-seeming position like, "Yes, we could solve this problem with machine learning, but it would actually cost more money than paying humans to do it by hand." Part of why I hate hate hate the term "AI" is because you simply can't call something artificial intelligence and then expect them to understand that it's not actually useful for doing anything that requires any kind of intelligence.
- mcguire 5y ago
  There's an old AI joke that all actual problems reduce to the artificial general intelligence problem---everything else, by definition, doesn't require intelligence.
  - mumblemumble 5y ago
    There's some truth to that. But I'd also argue that there's a tendency to try to bill every single kind of spinoff technology that the artificial intelligence community has produced as artificial intelligence. Which a bit like characterizing mixing up a glass of Tang as a kind of space exploration.
- 2sk21 5y ago
  You are absolutely correct. In fact, most NLP software ignores the formatting of documents which conveys a lot of information as well. For example, section headings must be treated differently from the text that makes up the body of a section. Its very hard to even determine section headings and then its hard to take advantage of them since the big transformer models simply accept a stream of unspecialized tokens.
  - deleted 5y ago
    [deleted]
jvanderbot 5y ago
In my field (space) we have an unspoken mantra that autonomous systems should aid human decision making. It's just so much easier to build a system that allows a human to focus on the tough calls, than it is to build an end-to-end system that makes all the decisions. Only in the most extreme examples does full autonomy make sense. If there were one doctor in the world, I'd build an autonomous mammogram machine and have him periodically audit the diagnoses. Otherwise, better tools is the way to go. I noticed this when visiting the OBGYN for sonograms to check the development of our children. The tools are really good. You can easily measure distances and thicknesses, visualize doppler flow of blood, everything is projected from weird polar space (what the instrument measures) to a Cartesian space (what we expect to see), and you can capture images or video in real time. Sure, the cabal factor is real, as is the curmudgeon doctor, but I think we should be building tools, not doctors. We know how to build doctors.
- divbzero 5y ago
  Building tools to aid humans seems like the best of both worlds. This is already happening in some radiology subspecialties: The autonomous systems can highlight potential areas of interest but it’s up to humans to make the final call. For some cases it’s a quick and easy call, but for tougher calls the radiologist can bring other factors into consideration.
- haihaibye 5y ago
  A counter example from your field: Would a human add value adjusting the boosters to vertically land starship?
  - jvanderbot 5y ago
    Not a counter example, but a legit question in spirit since autonomy vs human will always be a reasonable choice for some parts of a system. I just don't think real-time control of a system that's never been flown by humans is a good example of where that choice is hard. You can describe it with equations, solutions are well understood, there's no available humans that can do it, there's a well-understood need because fly by wire was necessitated to control some aircraft, and will be necessary to control more air/spacecraft in the future. Classic computer problem. (But I'm guessing Starship will have human controllers in/on the loop to do interventions before or during landing if required.) Doctor diagnosis is a good example of when that choice is hard to make. Hard to describe with equations, hard to model, not a well-understood need since professional doctors exist, decades of experience has been accumulated, huge swaths of industry are dedicated to helping doctors, etc etc. What I meant by "Allow the human to focus" definitely should be interpreted to mean removing small, multi-dimensional, fast, annoying things like real-time stabilization during landing.
awillen 5y ago
But isn't this just what ML should be good at - taking a huge number of data points and finding the patterns? Or are you saying it's not an issue of ML working poorly but rather one of there not being a good enough data set to train it on properly?
- tomrod 5y ago
  The gap is interpretation and application of those patterns. Building expert systems is expensive, but ML hits the low hanging fruit of showing patterns to experts out of the park.
- ethbr0 5y ago
  ML should be good at drawing basic conclusions. End users are misunderstanding the boundary between basic and advanced. Or, to put it another way, everyone agrees there's a difference in value and quality of output between an analyst with 1 year & 10 years of experience, right? So why are we treating ML like it should be able to solve both sorts of problems equally easily? I have faith it will get there. But it's not there yet, in a general purpose way.
  - mcguire 5y ago
    Because people like Hinton are outright saying that it already is there.
- azalemeth 5y ago
  One of the main tasks that doctors do is take patient's vague and non-specific problems, build up a rapport with them, understand what is normal and what is not, dealing with the "irrelevant" information present at the time, and focus the results into a finite tree of possibilities. In principle, this would be a great task for an ML algorithm. It's all conditional probability. But every such system has failed to do that well -- because the "gut feeling" the doctor develops is funded by a whole host of prior information that an ML algorithm won't be trained on: what is "normal" for an IV-drug addict, or a patient with psychosis; how significant is the "I'm a bit confused" in the middle-aged man who was cycling and came off his bike and hit his head? Do the burns on the soles of this child's feet sit consistently with the story of someone who ran over a fire-pit barbecue "for fun", or is it a non-accidental injury? It's a world of shades of grey where, if you call it the wrong way, ultimately someone could die. Doctors do Bayesian maths, and they do it with priors coming from both their own personal experience as a member of society, and professional training. That is, in my ignorant opinion, the main distinction between what I do -- oft-called "academic medicine" or "academic radiology" -- and clinical medicine. The former looks at populations. The latter looks at individuals. In other words, I don't think it's even possible to codify what the data the ML algorithms should be trained on -- they're culturally specific down to the level of an individual town in some sense; and require looking at huge populations at others.
wutbrodo 5y ago
> I spent 10 years in mammography software, and I saw first hand how many outside factors can impact a physician's decision to biopsy or not a given artifact on an image. This is slightly tangential, but I'm curious about your perspective on a classic example of medical statistical illiteracy. Whenever surveyed, the strong majority of doctors vastly overestimate the odds of a true positive mammogram (half of them by a factor of 10!!), due to flawed Bayesian thinking and the low base rate of breast cancer.[1] Does your anecdotal experience contradict this data? If not, wouldn't two minutes of stats education (or a system that correctly accounted for base rate) utterly swamp intuition-driven analysis of tiny artifacts? Or is it simply that, through folk wisdom and experience, they implicitly adjust other terms in their mental calculus in order to account for this huge error in estimating one factor? [1] https://blogs.cornell.edu/info2040/2014/11/12/doctors-dont-know-bayes-theorem/#:~:text=The%20probability%20that%20a%20woman,(%E2%80%9Cfalse%20alarm%20rate%E2%80%9D) https://blogs.cornell.edu/info2040/2014/11/12/doctors-dont-k...
- mjburgess 5y ago
  The doctor diagnosing a patient isn't solving a puzzle of the kind posed here. They are doing, as the previous comment said, interpretation. In practice, much of their thinking is profoundly rational and bayesian. Human (and animal) thinking isn't primarily cognitive, ie., explicit reasoning. It is the application of learned concepts to sensory-motor systems in the right manner. We don't look to dr's to formulate a crossword puzzle when a patient arrives; we look to them to be overly attentive to yellow skin when the patient's family has a history of alcoholism.
  - gmadsen 5y ago
    I'm not convinced this just couldn't be a large personal data set into an algorithm. Doctors barely have any data as it is. I think personal bio testing and monitoring is going to be a huge market and medical paradigm shift. would you rather have you heart rate and temp constantly monitored for months , or get it checked once a year by a GP to see if you have hypertension or any negative markers
    - 5y ago
nradov 5y ago
Incorporating more variables into the model wouldn't be sufficient. You would also need to get the input data for those variables into a form that algorithm could consume. Often the raw data simply isn't recorded in the patient's chart, or if it is recorded it's in unstructured text where even sophisticated NLP struggles to extract accurate discrete clinical concept codes.
mikesabbagh 5y ago
Mammography is one of the most difficult to interpret. You need more data like the age and family history to decide on the next step. Radiology is huge. I am sure ML can help in some of the specialties (it does not need to be all or none). The reason it is not is because of the medical system refusing to give in.

mumblemumble 5y ago

I see the same thing in natural language processing. A lot of important details come from outside the four corners of the document. Ironically, I often find myself in the unenviable position of being the machine learning person who's trying to convince people that machine learning is probably not a good fit for their problem. Or, worse, taking some more fuzzy-seeming position like, "Yes, we could solve this problem with machine learning, but it would actually cost more money than paying humans to do it by hand." Part of why I hate hate hate the term "AI" is because you simply can't call something artificial intelligence and then expect them to understand that it's not actually useful for doing anything that requires any kind of intelligence.

mcguire 5y ago
There's an old AI joke that all actual problems reduce to the artificial general intelligence problem---everything else, by definition, doesn't require intelligence.
- mumblemumble 5y ago
  There's some truth to that. But I'd also argue that there's a tendency to try to bill every single kind of spinoff technology that the artificial intelligence community has produced as artificial intelligence. Which a bit like characterizing mixing up a glass of Tang as a kind of space exploration.
2sk21 5y ago
You are absolutely correct. In fact, most NLP software ignores the formatting of documents which conveys a lot of information as well. For example, section headings must be treated differently from the text that makes up the body of a section. Its very hard to even determine section headings and then its hard to take advantage of them since the big transformer models simply accept a stream of unspecialized tokens.
- deleted 5y ago
  [deleted]

jvanderbot 5y ago

In my field (space) we have an unspoken mantra that autonomous systems should aid human decision making. It's just so much easier to build a system that allows a human to focus on the tough calls, than it is to build an end-to-end system that makes all the decisions. Only in the most extreme examples does full autonomy make sense. If there were one doctor in the world, I'd build an autonomous mammogram machine and have him periodically audit the diagnoses. Otherwise, better tools is the way to go. I noticed this when visiting the OBGYN for sonograms to check the development of our children. The tools are really good. You can easily measure distances and thicknesses, visualize doppler flow of blood, everything is projected from weird polar space (what the instrument measures) to a Cartesian space (what we expect to see), and you can capture images or video in real time. Sure, the cabal factor is real, as is the curmudgeon doctor, but I think we should be building tools, not doctors. We know how to build doctors.

divbzero 5y ago
Building tools to aid humans seems like the best of both worlds. This is already happening in some radiology subspecialties: The autonomous systems can highlight potential areas of interest but it’s up to humans to make the final call. For some cases it’s a quick and easy call, but for tougher calls the radiologist can bring other factors into consideration.
haihaibye 5y ago
A counter example from your field: Would a human add value adjusting the boosters to vertically land starship?
- jvanderbot 5y ago
  Not a counter example, but a legit question in spirit since autonomy vs human will always be a reasonable choice for some parts of a system. I just don't think real-time control of a system that's never been flown by humans is a good example of where that choice is hard. You can describe it with equations, solutions are well understood, there's no available humans that can do it, there's a well-understood need because fly by wire was necessitated to control some aircraft, and will be necessary to control more air/spacecraft in the future. Classic computer problem. (But I'm guessing Starship will have human controllers in/on the loop to do interventions before or during landing if required.) Doctor diagnosis is a good example of when that choice is hard to make. Hard to describe with equations, hard to model, not a well-understood need since professional doctors exist, decades of experience has been accumulated, huge swaths of industry are dedicated to helping doctors, etc etc. What I meant by "Allow the human to focus" definitely should be interpreted to mean removing small, multi-dimensional, fast, annoying things like real-time stabilization during landing.

awillen 5y ago

But isn't this just what ML should be good at - taking a huge number of data points and finding the patterns? Or are you saying it's not an issue of ML working poorly but rather one of there not being a good enough data set to train it on properly?

tomrod 5y ago
The gap is interpretation and application of those patterns. Building expert systems is expensive, but ML hits the low hanging fruit of showing patterns to experts out of the park.
ethbr0 5y ago
ML should be good at drawing basic conclusions. End users are misunderstanding the boundary between basic and advanced. Or, to put it another way, everyone agrees there's a difference in value and quality of output between an analyst with 1 year & 10 years of experience, right? So why are we treating ML like it should be able to solve both sorts of problems equally easily? I have faith it will get there. But it's not there yet, in a general purpose way.
- mcguire 5y ago
  Because people like Hinton are outright saying that it already is there.
azalemeth 5y ago
One of the main tasks that doctors do is take patient's vague and non-specific problems, build up a rapport with them, understand what is normal and what is not, dealing with the "irrelevant" information present at the time, and focus the results into a finite tree of possibilities. In principle, this would be a great task for an ML algorithm. It's all conditional probability. But every such system has failed to do that well -- because the "gut feeling" the doctor develops is funded by a whole host of prior information that an ML algorithm won't be trained on: what is "normal" for an IV-drug addict, or a patient with psychosis; how significant is the "I'm a bit confused" in the middle-aged man who was cycling and came off his bike and hit his head? Do the burns on the soles of this child's feet sit consistently with the story of someone who ran over a fire-pit barbecue "for fun", or is it a non-accidental injury? It's a world of shades of grey where, if you call it the wrong way, ultimately someone could die. Doctors do Bayesian maths, and they do it with priors coming from both their own personal experience as a member of society, and professional training. That is, in my ignorant opinion, the main distinction between what I do -- oft-called "academic medicine" or "academic radiology" -- and clinical medicine. The former looks at populations. The latter looks at individuals. In other words, I don't think it's even possible to codify what the data the ML algorithms should be trained on -- they're culturally specific down to the level of an individual town in some sense; and require looking at huge populations at others.

wutbrodo 5y ago

> I spent 10 years in mammography software, and I saw first hand how many outside factors can impact a physician's decision to biopsy or not a given artifact on an image. This is slightly tangential, but I'm curious about your perspective on a classic example of medical statistical illiteracy. Whenever surveyed, the strong majority of doctors vastly overestimate the odds of a true positive mammogram (half of them by a factor of 10!!), due to flawed Bayesian thinking and the low base rate of breast cancer.[1] Does your anecdotal experience contradict this data? If not, wouldn't two minutes of stats education (or a system that correctly accounted for base rate) utterly swamp intuition-driven analysis of tiny artifacts? Or is it simply that, through folk wisdom and experience, they implicitly adjust other terms in their mental calculus in order to account for this huge error in estimating one factor? [1] https://blogs.cornell.edu/info2040/2014/11/12/doctors-dont-know-bayes-theorem/#:~:text=The%20probability%20that%20a%20woman,(%E2%80%9Cfalse%20alarm%20rate%E2%80%9D) https://blogs.cornell.edu/info2040/2014/11/12/doctors-dont-k...

mjburgess 5y ago
The doctor diagnosing a patient isn't solving a puzzle of the kind posed here. They are doing, as the previous comment said, interpretation. In practice, much of their thinking is profoundly rational and bayesian. Human (and animal) thinking isn't primarily cognitive, ie., explicit reasoning. It is the application of learned concepts to sensory-motor systems in the right manner. We don't look to dr's to formulate a crossword puzzle when a patient arrives; we look to them to be overly attentive to yellow skin when the patient's family has a history of alcoholism.
- gmadsen 5y ago
  I'm not convinced this just couldn't be a large personal data set into an algorithm. Doctors barely have any data as it is. I think personal bio testing and monitoring is going to be a huge market and medical paradigm shift. would you rather have you heart rate and temp constantly monitored for months , or get it checked once a year by a GP to see if you have hypertension or any negative markers
  - 5y ago

nradov 5y ago

Incorporating more variables into the model wouldn't be sufficient. You would also need to get the input data for those variables into a form that algorithm could consume. Often the raw data simply isn't recorded in the patient's chart, or if it is recorded it's in unstructured text where even sophisticated NLP struggles to extract accurate discrete clinical concept codes.

mikesabbagh 5y ago

Mammography is one of the most difficult to interpret. You need more data like the age and family history to decide on the next step. Radiology is huge. I am sure ML can help in some of the specialties (it does not need to be all or none). The reason it is not is because of the medical system refusing to give in.