Row 17335

Row ID: 17335 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 17335 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I can't really speak to what's going on in Bio/Pharma, but when it comes to health systems (like hospitals and clinics) trying to implement and reap value from ML, its been underwhelming in my opinion.

The problems have nothing to do with ML or medicine. Bureaucracy and red tape at large hospitals is almost as bloated as the government - makes things like getting data and securing a budget move at a snails pace. Also, the folks building the systems usually know little about what a clinician does day-to-day and that means they'll focus on the wrong problems before realizing where the important gaps are. I think the biggest thing, that no one has quite figured out is that you need to be so operationally excellent to go from a trained model to having a team of hundreds of clinician using it daily, tracking and auditing both the model and the clinicians output, continually improving the process until you can confirm you have a working system.

Anyways, I've been knee-deep in healthcare AI for some time now, so I'm probably a bit jaded. Take what I say with a grain of salt.

Here's an article that talks about where ML didn't move the needle when it should have: [https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/](https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/)

Here's one where some amazing results have come out of leading hospitals: [https://www.nature.com/articles/s41591-022-01894-0](https://www.nature.com/articles/s41591-022-01894-0)

FieldValue
text I can't really speak to what's going on in Bio/Pharma, but when it comes to health systems (like hospitals and clinics) trying to implement and reap value from ML, its been underwhelming in my opinion. The problems have nothing to do with ML or medicine. Bureaucracy and red tape at large hospitals is almost as bloated as the government - makes things like getting data and securing a budget move at a snails pace. Also, the folks building the systems usually know little about what a clinician do…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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Raw Record

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  "text": " I can't really speak to what's going on in Bio/Pharma, but when it comes to health systems (like hospitals and clinics) trying to implement and reap value from ML, its been underwhelming in my opinion. \n\nThe problems have nothing to do with ML or medicine. Bureaucracy and red tape at large hospitals is almost as bloated as the government - makes things like getting data and securing a budget move at a snails pace. Also, the folks building the systems usually know little about what a clinician does day-to-day and that means they'll focus on the wrong problems before realizing where the important gaps are. I think the biggest thing, that no one has quite figured out is that you need to be so operationally excellent to go from a trained model to having a team of hundreds of clinician using it daily, tracking and auditing both the model and the clinicians output, continually improving the process until you can confirm you have a working system. \n\nAnyways, I've been knee-deep in healthcare AI for some time now, so I'm probably a bit jaded. Take what I say with a grain of salt. \n\nHere's an article that talks about where ML didn't move the needle when it should have: [https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/](https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/)\n\nHere's one where some amazing results have come out of leading hospitals: [https://www.nature.com/articles/s41591-022-01894-0](https://www.nature.com/articles/s41591-022-01894-0)",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
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