Surprise, surprise. People hugely overestimate the data retrieval capabilities of healthcare systems. And if you really put clinical 'AI' systems to the test in day-to-day settings (which is in fact never done), results would be much, much worse. Shit data in, shit prediction out.
Look up prospective validation or clinical impact trials. These are validated in day-to-day settings somewhat often, though not as often as they should be.
I'm a professional clinical researcher. Those 'validations' would be comical if the results, when witnessed in person, weren't so sad.
How many specialists did you go to before it was identified? How many other people with the condition were misidentified? I only say this because of a family member with a rare genetic condition. For years they were told it was something else, or told 'it was in their head'. The family member started a journal of their medical conditions and experiences that was detailed then brought that to their PC which whom then…
> How many specialists did you go to before it was identified? 2 opthalmo, 1 internal medicine, 1 retina super-specialist & finally someone from USC Davey > How many other people with the condition were misidentified? Historical data: I don't know. It is fairly divided between two types, one being zoonotic & other to IL2 gene. I am told this distinction of pathways was identified in 2007. > [..] you kept a detailed l…
This escalation is going through a range of increasingly more specialized experts is a critical part if a difgicult diagnosis. The process endures different unique perspectives.
> Are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? Personal story: I was diagnosed with a rare genetic disease in 2019. If I ran the symptoms through a ML gauntlet, I would be sure they would cancel each other out or make little sense. Chest CT (clean), fever (high), TB test (negative), laten…
How many specialists did you go to before it was identified? How many other people with the condition were misidentified? I only say this because of a family member with a rare genetic condition. For years they were told it was something else, or told 'it was in their head'. The family member started a journal of their medical conditions and experiences that was detailed then brought that to their PC which whom then…
That all sounds shitty but I don't see how that's valuable information. They did eventually solve the problem and there's no comparison to some ML success story.
Human minds can be really good at diagnostics and still fail sometimes when faced with very difficult cases.
In my experience, ML would just classify everything as a very common disease and people would call it a success because it has an 80% effectiveness rate.
The problem that needs to be solved is a case like your example, not diagnosing the common cold.
My view on this is framed a bit differently but probably a similar ultimate perspective: I think it's probably going to be a long time before models only using quantifiable measurements can even meet the performance of top doctors. I can't recommend enough that someone experiencing issues doctor-shop if they haven't gotten a well-explained diagnosis from their current doctor. But I'm very curious how good one has to…
> But I'm very curious how good one has to be in order to be better than a below-average doctor, or a 50th-percentile doctor, or a 75th. In dermatology, on which I was working, models were better (at detecting skin cancers) than 52% of the GPs, going by just images. In a famous Nature paper by Esteva et al., the TPR was at 74% for detecting Melanomas. There is a catch which probably got underreported (The skin cancer…
Isn't it a tad unfair to compare a ML model for dermatology, only working with pictures, against general practitioners? IMHO comparing said model against dermatologists would be a better approach. And just working from images is not necessarily a dermatological model, buy rather an image analysis model.
Interesting, could you explain more about the clinical markings? Was this mentioned in the paper itself or was it later commentary?
I remember a similar New Yorker article ~5 years ago about medical imaging ML/AI where they realized it's good hit rate was actually a data artifact from training. Something along the lines that essentially all the positives had secondary scans and so there was a set of known positives which had say run through the same machine/lab and had similar label color & text markings in the margin of the imaging. When they we…
Nice, a model identifying the cases a doctor had enough doubts about to have a second test run.
are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.
> I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease. I suspect software engineers massively underestimate the value of skills outside their domain.
The good ones don't, they also realize the value of working with experts from the field they develop software for and with. And ues, that includes business people. The bad ones they can replace decades worth of experience, training and eductaion in certain field with deducting the necessary insight with first principle thinking.
The same applies not just for software devs, but for every other domain as well.
are you really sure the doctors are doing a better job when they go through the motions of incorporating a wide range of data? Or do we just convince ourselves they're better? I suspect we massively underestimate the amount of misdiagnosis due to incorrect analysis of data using fairly naive medical mental models of disease.
Not this ignorant comment again. AI will replace software engineers long before it replaces doctors. There is an arrogant ignorance of what doctors do that always shows up in comments when topics like this pop up. And yes I'm a physician and MLE. So i understand both worlds clearly
But we all saw House, Grey's Anatomy and Emergency Room. So we know exactly what doctors do!
I worked in healthcare ML solutions, as part of my PhD & also as consultant to a telemedicine company. My experience in dealing with data (we had sufficient, and somewhat well labeled) & methods made me realize that a lot of the prediction human doctors make are multimodal - and that is something deep learning will struggle for the time being. For example, say in detection of a disease X , physicians factor in blood…
> We humans are incredibly good at elimination of factors & differential diagnosis. I don't automatically buy this. Didn't heart attack care in the ER get dramatically better when people started following checklists? That suggests that human doctors aren't that great at even getting the basics correct. In addition, most doctors are below average . So, maybe the best doctors are better than the AI. However, I may not…
Using check lists means stabdardization, and that makes results compareable and reduces the risk of forgetting something under stress. Check lists have nothing to do with ML or AI so.
Thanks for sharing. My belief is that we need to figure out a way to make humans interact with prediction models in a virtuous way. Prediction models suck at "connecting the dots" or considering multiple sources of information (for example: multiple models predicting different outcomes). Until we get true general artificial intelligence, I think the way to go forward is to try to quantify those unknowns through confi…
Personally knowing the hit rate of these ML models & their non-explanatory nature, weighed against their low cost.. I'd argue they should be used as a default automated second opinion to radiologist opinion. Recently went through a pet cancer death so though medical imaging, diagnostic testing, specialist escalation and second opinion workflows are pretty fresh in my mind. There is a shortage of specialists, backlog…
In my experience, multiple knee MRTs, the liver, the intestine, the ankle..., radiologists by default are the second opinion to the specialist, e.g. an oncologist, that sent you to get the scan in the first place. I never ever had a radiologist come up with a diagnosis by himself.