Row 22542

Row ID: 22542 | Dataset Entry | Axioma AXP Content Repository

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I think the issue here isn't that AI has no success stories. It's that in practice, there's not going to be a drug that's "designed by AI". Instead, there are going to be a bunch of tools that are built using AI, and are used in conjunction with other techniques.

For instance, as you mentioned, AI is widely used in interpreting medical imagery. But it's not used to tell patients the results of their tests. It's used to highlight things that doctors should take a closer looks at. Similarly, machine learning is used in a lot of drug discovery, but not in the final step, because those AI applications are used as one part of the toolbox by researchers who have advanced degrees and know how to interpret that data in context alongside a lot of other things.

There are a lot of reasons for this. Some of those reasons are based in systems that are slow to change, especially when they are heavily regulated (e.g., the FDA) or when there are major financial stakes (e.g., agreements between insurance companies and medical practices about what is medically necessary in different situations). But it would also be dumb to just throw away everything we know about the practice of medicine and cede the field to AI models. AI isn't the only valid and useful way to make decisions here.

Just a random bit of personal experience. I needed an EKG stress test a few years ago. It was kind of cool to see the technician who did the EKG take images via ultra-sound, watch the machine learning algorithm draw in the boundaries of various chambers they wanted to measure the volume of, etc., and the technician then just double check and make minor adjustments. Definitely made the process smoother, and the test more accurate. But that technician still took fundamental responsibility for the accuracy of that test.

FieldValue
text I think the issue here isn't that AI has no success stories. It's that in practice, there's not going to be a drug that's "designed by AI". Instead, there are going to be a bunch of tools that are built using AI, and are used in conjunction with other techniques. For instance, as you mentioned, AI is widely used in interpreting medical imagery. But it's not used to tell patients the results of their tests. It's used to highlight things that doctors should take a closer looks at. Similarly,…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-21
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Raw Record

{
  "text": "I think the issue here isn't that AI has no success stories.  It's that in practice, there's not going to be a drug that's \"designed by AI\".  Instead, there are going to be a bunch of tools that are built using AI, and are used in conjunction with other techniques.\n\nFor instance, as you mentioned, AI is widely used in interpreting medical imagery.  But it's not used to tell patients the results of their tests.  It's used to highlight things that doctors should take a closer looks at.  Similarly, machine learning is used in a lot of drug discovery, but not in the final step, because those AI applications are used as one part of the toolbox by researchers who have advanced degrees and know how to interpret that data in context alongside a lot of other things.\n\nThere are a lot of reasons for this.  Some of those reasons are based in systems that are slow to change, especially when they are heavily regulated (e.g., the FDA) or when there are major financial stakes (e.g., agreements between insurance companies and medical practices about what is medically necessary in different situations).  But it would also be dumb to just throw away everything we know about the practice of medicine and cede the field to AI models.  AI isn't the only valid and useful way to make decisions here.\n\nJust a random bit of personal experience.  I needed an EKG stress test a few years ago.  It was kind of cool to see the technician who did the EKG take images via ultra-sound, watch the machine learning algorithm draw in the boundaries of various chambers they wanted to measure the volume of, etc., and the technician then just double check and make minor adjustments.  Definitely made the process smoother, and the test more accurate.  But that technician still took fundamental responsibility for the accuracy of that test.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-21",
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}
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