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…
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…
Medicine's Machine Learning Problem
31–40 of 112 posts
Re: Medicine's Machine Learning Problem
#32I'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…
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…
Not OP, but anesthesiologist and hobby programmer for 15 years. What you are describing is a fundamental flaw of the current AI effort: the data that supports AI models is mostly irrelevant to the problem. In medicine, the saying goes: 90% of diagnoses are made on patient history. Ironically, there is no reason that would change for AI-enabled systems given the same information.
So to answer you directly yes, it's a better route until we have better information available. But it's also the wrong route to take in the long term. It would be far better to attempt to produce better supporting information.
Re: Medicine's Machine Learning Problem
#33The 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.
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.
Re: Medicine's Machine Learning Problem
#34I'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…
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.
Re: Medicine's Machine Learning Problem
#35The 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.
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.
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.
Re: Medicine's Machine Learning Problem
#36I'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…
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".
Re: Medicine's Machine Learning Problem
#37I'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…
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.
Re: Medicine's Machine Learning Problem
#38I'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…
May I suggest, in response to your sentiment that applications of AI to medicine are lacking, is that you are seeing applications replace current medical practices. An AI diagnosis of a medical image seems redundant indeed, however in this situation a patient has seen a doctor out of complaints and has been sent to the radiologist for further investigation. This medical practice is reactionary, and suspicions are alr…
Health policy is fraught with counter-intuitive phenomenon - and screening is one of them.
Seems like it should help, but in practice leads to over-diagnosis.
For example - Cancer rates jumped in Korea after screening with no impact on patient outcomes [1]. There are several others.
[1] Lee, J. H., & Shin, S. W. (2014). Overdiagnosis and screening for thyroid cancer in Korea. The Lancet, 384(9957), 1848.
Re: Medicine's Machine Learning Problem
#39Earlier 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…
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.
Re: Medicine's Machine Learning Problem
#40I'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…
> 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'm quite surprised by this. Doesn't each AI tutorial start by stating that very thing?