llm/e6f7e516-f0a0-4424-8f8f-157aae85c74e/topic-10-636e6af9-83a0-4880-a301-3b2a63ff0e99-input.json
The following is content for you to summarize. Do not respond to the comments—summarize them. <topic> Medical and Specialized Fields # Debates on AI in radiology and medicine. While some see potential in automated reporting and "second opinions" to catch errors, professionals argue that current models struggle with complex cases, over-report issues, and lack the nuance required for high-stakes diagnostics. </topic> <comments_about_topic> 1. > I've gotten a lot of value out of reading the views of experienced engineers; overall they like the tech, but they do not think it is a sentient alien that will delete our jobs. I normally see things the same way you do, however I did have a conversation with a podiatrist yesterday that gave me food for thought. His belief is that certain medical roles will disappear as they'll become redundant. In his case, he mentioned radiology and he presented his case as thus: A consultant gets a report + X-Ray from the radiologist. They read the report and confirm what they're seeing against the images. They won't take the report blindly. What changes is that machines have been learning to interpret the images and are able to use an LLM to generate the report. These reports tend not to miss things but will over-report issues. As a consultant will verify the report for themselves before operating, they no longer need the radiologist. If the machine reports a non-existent tumour, they'll see there's no tumour. 2. I doubt this simply because of the inertia of medicine. The industry still does not have a standardized method for handling automated claims like banking. It gets worse for services that require prior authorization; they settle this over the phone ! These might sound like irrelevant ranting, but my point is that they haven't even addressed the low-hanging fruits, let alone complex ailments like cancer. 3. IMO prior authorization needing to be done on the phone is a feature, not a bug. It intentionally wastes a doctor's time so they are less incentivized to advocate for their patients and this frustration saves the insurance companies money. 4. Heard. I do wonder why hospitals haven't automated their side though. Regardless, the recent prior auth situation is a trainwreck. If I were dictator, insurance companies would be non-profit and required to have a higher loss ratio. 2 quibbles: 1) a more ethical system would still need triage-style rationing given a finite budget, 2) medical providers are also culpable given the eye-watering prices for even trivial services. 5. I've seen this sort of thing a few times. "Yes, I'm sure AI can do that other job that's not mine over there.". Now maybe foot doctors work closer to radiologists than I'm aware of. But the radiologists that I've talked to aren't impressed with the work AI had managed to do in their field. Apparently there are one or two incredibly easy tasks that they can sort of do, but it comprises a very small amount of the job of an actual radiologist. 6. > But the radiologists that I've talked to aren't impressed with the work AI had managed to do in their field. Just so I understand correctly: is it over-reporting problems that aren't there or is it missing blindingly obvious problems? The latter is obviously a problem and, I agree, would completely invalidate it as a useful tool. The former sounded, the way it was explained to me, more like a matter of degrees. 7. I'm afraid I don't have the details. I was reading about certain lung issues the AI was doing a good job on and thought, "oh well that's it for radiology." But the radiologist chimed in with, "yeah that's the easiest thing we do and the rates are still not acceptable, meanwhile we keep trying to get it to do anything harder and the success rates are completely unworkable." 8. AI luminary and computer scientist Geoffrey Hinton predicted in 2016 that AI would be able to do all of the things radiologists can do within five years. We're still not even close. He was full of shit and now almost 10 years later he's changed his prediction, though still pretending he was right, by moving the goal posts. His new prediction is that radiologists will use AI to be more efficient and accurate, half suggesting he meant that all along. He didn't. He was simply bullshitting, bluffing, making an educated wish. This is the nonsense we're living through, predictions, guesses, promises that cannot possibly be fulfilled and which will inevitably change to something far less ambitious and with much longer timelines and everyone will shrug it off as if we weren't being mislead by a bunch of fraudsters. 9. I was at a podiatrist yesterday who explained that what he's trying to do is to "train" an LLM agent on the articles and research papers he's published to create a chatbot that can provide answers to the most common questions more quickly than his reception team can. He's also using it to speed up writing his reports to send to patients. Longer term, he was also quite optimistic on its ability to cut out roles like radiologists, instead having a software program interpret the images and write a report to send to a consultant. Since the consultant already checks the report against any images, the AI being more sensitive to potential issues is a positive thing: giving him the power to discard erroneous results rather than potentially miss something more malign. 10. > Longer term, he was also quite optimistic on its ability to cut out roles like radiologists, instead having a software program interpret the images and write a report to send to a consultant. As a medical imaging tech, I think this is a terrible idea. At least for the test I perform, a lot of redundancy and double-checking is necessary because results can easily be misleading without a diligent tech or critical-thinking on the part of the reading physician. For instance, imaging at slightly the wrong angle can make a normal image look like pathology, or vice versa. Maybe other tests are simpler than mine, but I doubt it. If you've ever asked an AI a question about your field of expertise and been amazed at the nonsense it spouts, why would you trust it to read your medical tests? > Since the consultant already checks the report against any images, the AI being more sensitive to potential issues is a positive thing: giving him the power to discard erroneous results rather than potentially miss something more malign. Unless they had the exact same schooling as the radiologist, I wouldn't trust the consultant to interpret my test, even if paired with an AI. There's a reason this is a whole specialized field -- because it's not as simple as interpreting an EKG. 11. I'm mostly a fan of AI coding tools, but I think you're basically right about this. I think we'll see more specialized models for narrow tasks (think AlphaFold for other challenges in drug discovery, for example) as well, but those will feel like individual, costly, high impact discoveries rather than just generic "AI". Our world is human-shaped and ultimately people who talk of "AGI" secretly mean an artificial human. I believe that "intelligence", the way the word is actually used by people, really just means "skillful information processing in pursuit of individual human desires". As such, it will never be "solved" in any other way than to build an artificial human. </comments_about_topic> Write a concise, engaging paragraph (3-5 sentences) summarizing the key points and perspectives in these comments about the topic. Focus on the most interesting viewpoints. Do not use bullet points—write flowing prose.
Medical and Specialized Fields # Debates on AI in radiology and medicine. While some see potential in automated reporting and "second opinions" to catch errors, professionals argue that current models struggle with complex cases, over-report issues, and lack the nuance required for high-stakes diagnostics.
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