Row 27463

Row ID: 27463 | Dataset Entry | Axioma AXP Content Repository

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Yes in some way, No in a lot of ways from my experience (oncologist and AI PhD student). Many things have limitation only people from healthcare knows.

There is no such thing as "unbiased ML" unless you specifically design and curate the data to be that way. You can't just throw in a bunch of EMR data, slap on buzz word like "real world data" and call it a day or unbiased. This is beyond stupid and unfortunately is done too often.

We have had prediction model for literally decades, like simple decision tree or logistic regression. Some has amazing performance, rivaling ML, but has not been adopted clinically yet. Partly, it is due to bias of physician to distrust algorithm. Also, physician just suck ass in adopting new methods due to inertia or whatever, but there are also bajillions of other reasons like:

* The model is too cumbersome, too much input feature, the feature needed is not available at the decision point to make it relevant. * The model is not that clinically useful (it predicts useless label or endpoint which we nor the patient do not care, or care but have no way of actually doing anything about that). * The model is trained on a very specific subset of patient on a specific subset of hospital which is not widely applicable. * The model is outdated, since new knowledge from RCT superseded what the model has been trained on. * The model can not be easily accessed when I want to (not accessible through browser, only available as standalone program or worse, only Github code) * Personalize medicine has not really proven to be "that much" more beneficial then just assume average treatment effect. The thing is, you need to prove that precision or personalized medicine "using ML" is better than me just using traditional "focused biology" or just plain decision tree. An ML model with high balanced accuracy, F-1 or Brier means nothing if you do not try to convince, or even better, demonstrate it's superiority to what we have been doing. The model might be somewhat better in some subset of patient of some disease. Yet we treat like, what, 100+ disease? With the headache of EMR, I cannot possibly have the time to bookmark and save all AI model for use for 100+ disease. In practice, I only remember some model to be used in certain situation only, and a lot of that is not ML. In theory, a model to choose what model to use based on LLM or some tech stack or whatever is certainly possible but I have not seen it done in practice yet. * and trillions other things.

The thing is that AI/ML engineer like to push their ML solution to healthcare and over-hype their models. I shudder every time and I am damn tired of attending a conference and someone try to sell their model without clearly demonstrating what value the model will add to the care except "our model has good performance"

That being said, there are areas where I am excited in as well:

* Signal, imaging pathology, as many have said. Many are already being used and we are just barely scratching the surface on that. * Drug discovery. Tons of potential. * LLM, with all it's caveat and hallucination, might help as a kind of companion or anchor to assist clinician, identifying cognitive bias and the likes. * Casual inference. This is not limited to ML actually but our field is lacking behind as compared to fields like economics in deriving casual claims from retrospective data. So many question that can be answer but the knowledge has not been properly utilized yet. * Automation of boring stuff (think transcription or something like that). I actually want this as the top priority.

FieldValue
text Yes in some way, No in a lot of ways from my experience (oncologist and AI PhD student). Many things have limitation only people from healthcare knows. There is no such thing as "unbiased ML" unless you specifically design and curate the data to be that way. You can't just throw in a bunch of EMR data, slap on buzz word like "real world data" and call it a day or unbiased. This is beyond stupid and unfortunately is done too often. We have had prediction model for literally decades, like simple…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-21
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Raw Record

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  "label": "r/machinelearning",
  "dataType": "comment",
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  "datetime": "2024-05-21",
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