11–20 of 162 posts
As 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…
What amount of energy is necessary for an event to be reflected in a statistic? You have a box of 2x2 meters with balls of data, and a string with a diameter of 1 meter with which to surround the highest concentration of balls possible, and those that remain outside, there they stay. Statistics and lack of precision are concepts that go hand in hand (someones say even it is not an science).
As 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…
That scenario sounds like it lends itself more to AI automation than a Dr. House type one.
My humble opinion; AI is supposed to be the acronym for artificial intelligence, but marketing has usurped it to refer to machine learning, which is nothing more than a neo-language for defining statistical equations in a semi-automated way. An attempt to dispense with mathematicians to develop models. What amount of energy is necessary for an event to be reflected in a statistic? You have a box of 2x2 meters with ba…
What’s most peculiar to me is that somehow AI is becoming more distinct from math or stats and that there’s a notion by running pytorch one is able to play god and create sentience.
Basically, we need an accurate measure of whether the test data set is statistically representative of wild data. In healthcare, this means that the individuals that make of the test dataset must be statistically representative of the actual population (and also have enough samples).
An easy solution here is that any research that doesn't pass a population statistics test must be up-front declared to be "not representative of real word usage" or something.
I 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 is entirely unsurprising and has a very simple solution: keep adding more data. Our measurements of the accuracy of AI systems are only as good as the test data, and if the test data is too small, then the reported accuracies won't reflect the true accuracies of the model applied to wild data. Basically, we need an accurate measure of whether the test data set is statistically representative of wild data. In hea…
> Here’s why: As researchers feed data into AI models, the models are expected to become more accurate, or at least not get worse. However, our work and the work of others has identified the opposite, where the reported accuracy in published models decreases with increasing data set size.
I 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…
Sorry for asking, but how is this relevant to the article?
1. Involved in the build process
2. Never touched until paper metrics are being written, only run once
Realistically, unlikely to occur however due to the incentives causing publication bias.