So many in ai chasing software solutions when the problem is hardware. Limited power means limited learning. Mix lab grown neurons with software and you have a wining proposition.
AI models that predict disease are not as accurate as reports might suggest
81–90 of 162 posts
Re: AI models that predict disease are not as accurate as reports might suggest
#82I 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…
I’m confused by your comment because these are exactly the type of problems that humans generally really do a poor job classifying.
Re: AI models that predict disease are not as accurate as reports might suggest
#83I recently published a paper, where we explain how an FDA approved prediction model, build into a widely used cardiac monitor was developed with an incredibly biased method. https://doi.org/10.1097/ALN.0000000000004320 Basically, the training and validation data was engineered so an important range for one of the predictor variables was only present in one of the outcomes, making perfect prediction possible for these…
This has to be intentional no?
Re: AI models that predict disease are not as accurate as reports might suggest
#84I 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…
The system itself should be built around these capabilities, not the other way around. Instead of collecting data at regular intervals we wait until symptoms to go to the doctor. This is why the dataset is so sparse.
Exactly this. The features (or limitations) of medical data is inherent in the process of clinical practice, but this seems to be oftentimes overlooked.
Re: AI models that predict disease are not as accurate as reports might suggest
#85I 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…
I’m confused by your comment because these are exactly the type of problems that humans generally really do a poor job classifying.
Most modern ML techniques do a poor job on these types of problems too unless they have a lot of data (hence the reference to sparsity) or assume structure that requires domain specific modeling to capture.
Re: AI models that predict disease are not as accurate as reports might suggest
#86Yet.
Re: AI models that predict disease are not as accurate as reports might suggest
#87As always, AI performs poorly in the real world until they don't.
Re: AI models that predict disease are not as accurate as reports might suggest
#88As someone who works in healthcare, so much of what I read about AI makes me think that the people who are enthusiastic about healthcare AI don't have much experience doing it. The scenarios rarely seem to fit with what I'm actually practicing. Most of medicine is boring, it is largely routine, and if we don't know what's going on, it's because we're not the right person to be managing the patient. Most of my time is…
So much this. I just interviewed about 10 doctors in the space of neurology and radiology to start some new projects. The truth is most of the headaches are from insurance coverage check or for radiologist for filling out correct reports. The fancy AI stuff is with maybe a few exceptions due to the great advancement imaging still far away from validation and I didn’t even start about it’s usage and gotomarket. Most o…
I'm a physician and former engineer and I agree with you. Even the giant tech companies that are in this space seem to have very little insight into how to utilize AI to improve healthcare.
The mistake everyone seems to make is thinking that making a diagnosis is what makes medicine hard. It's not. e.g. An algorithm that can diagnose diabetic retinopathy from fundus photos 'as well as a specialist' makes for a good headline, but isn't particularly useful. Medicine is nothing like "House" in real life. AI should be leveraged to either improve efficiency or develop novel tools.
Re: AI models that predict disease are not as accurate as reports might suggest
#89I recently published a paper, where we explain how an FDA approved prediction model, build into a widely used cardiac monitor was developed with an incredibly biased method. https://doi.org/10.1097/ALN.0000000000004320 Basically, the training and validation data was engineered so an important range for one of the predictor variables was only present in one of the outcomes, making perfect prediction possible for these…
This has to be intentional no?
> I decline to answer that question on the grounds that it might be used to incriminate me
Re: AI models that predict disease are not as accurate as reports might suggest
#90Earlier quoted context omitted.
I’m confused by your comment because these are exactly the type of problems that humans generally really do a poor job classifying.
Most modern ML techniques do a poor job on these types of problems too unless they have a lot of data (hence the reference to sparsity) or assume structure that requires domain specific modeling to capture.
No post body was provided.