> Even now, I'll take a surgeon that's a complete jerk over a nice surgeon any day, because if they've got that job even as a jerk they've got to be good at their jobs.
This seems like an incredibly poor line of reasoning.
Hospitals are often desperate for surgeons. The poorly mannered ones are often deeply unsatisfied, angry at the grueling lives they've opted into, and the hospitals can't replace them. The market is not exactly at work here.
So why not order your own labs? I'm sure you can think of ways to get your own medications if you are sufficiently convinced that this is the best course of action for your health.
Arterial blood gas. Calcium score (may not count as a lab). Skin biopsy for cancer (does it count as a lab?). I'm unaware how to order my own troponin if I think I've had a heart attack (not that that's one I should DIY diagnosis). Prostate specific antigen.
Several of those are more procedures than labs. Of course you can't get someone to do a procedure on you for free. Arterial sticks and biopsies may have nontrivial risks (and commensurate liability risk for the performing provider).
PSA and troponin seem trivial to get. Did you look?
Unfortunately the training data is absolute garbage.
Diagnostic standards in (at least emergency, but I think other specialties) medicine are largely a joke -- ultimately it's often either autopsy or "expert consensus."
We get to bill more for more serious diagnoses. The amount of patients I see with a "stroke" or "heart attack" diagnosis that clearly had no such thing is truly wild.
We can be sued for tens of millions of dollars for missing a serious diagnosis, even if we know an alternative explanation is more likely.
If AI is able to beat an average doctor, it will be due to alleviating perverse incentives. But I can't imagine where we could get training data that would let it be any less of a fountain of garbage than many doctors.
Without a large amount of good training data, how could AI possibly be good at doctoring IRL?
You just get 1M doctors to wear body cams for a year. Now you have a model that has thousands of times your experience with patients, encyclopedic knowledge of every ailment including ones that never present in your geography, read all the latest papers, etc..
I don't understand how you think this doesn't win vs a human doctor.
This wouldn't solve the problem of diagnostic standards. Let's say you are a pediatrician and want to predict which kids with bronchiolitis will develop respiratory failure and need the ICU versus the ones who can go home. How do you determine from the body cams which kids had bronchiolitis in the first place? Bronchiolitis is a clinical diagnosis with symptoms that overlap with other respiratory illnesses such as asthma, bacterial pneumonia, croup, foreign body ingestion, etc.
you would have footage of the doctors diagnosing them. I don't understand what you're asking. The body cams have microphones too in case that wasn't clear.
In healthcare, HIPAA/GDPR equivalent would block this. Let's be realistic in our discussion; this is not the same as google buying up a library worth of books, scanning and destroying them
Other countries actually don't necessarily have a similar mix of ailments, median patient appearance and style of communication or even recommended course of action and most of the ones with more sophisticated medical care also have strict medical privacy laws. If you're genuinely unaware of this, I'm not sure you're in a position to be making "one year with a camera, how hard can it be" arguments...
(Where AI is likely to actually excel in medicine is parsing datasets that are much easier to do context free number crunching on than ER rooms, some of which physicians don't even have access to ...)
I think you're being silly if you think the amount of money at stake here, not the mention the health of billions of people is going to be stymied by privacy laws.
We have wildly heterogeneous data just within the US!
And again, how exactly is this interface going to work? How does the AI determine how hard to press on an abdomen, and where, and how does it press there once it has that information?
I'm still fairly new to local LLMs, spent some time setting up and testing a few Qwen3.6-35B-A3B models yesterday (mlx 4b and 8b, gguf Q4_K_M and Q4_K_XL I think).
Was impressed at how they ran on my 64G M4.
It looks like this new model is slightly "smarter" (based on the tables in TFA) but requires more VRAM. Is that it? The "dense" part being the big deal?
As 27B < 35B, should we expect some quantized models soon that will bring the VRAM requirement down?
that's not it. 35B-A3B is a "Mixture of Experts" model. Roughly, only ~3B parameters are active at a time. So, the actual computational requirements scale with this ~3B, rather than with the 35B (though you need high-bandwidth access to the full 35B layers though).
This model is a "dense" model. It will be much slower on macs. Concretely, on a M4 Pro, at Q6 gguf, it was ~9tok/s for me. 35-A3B (at Q4, with mlx, so not a fair comparison) was ~70 tok/s by comparison.
In general dedicated GPUs tend to do better with these kinds of "dense" models, though this becomes harder to judge when the GPU does not have enough VRAM to keep the model fully resident. For this model, I would expect if you have >=24GB VRAM you'd be fine, e.g. an NVIDIA {3,4,5}090-type thing.
I strongly disagree. If it's doing well enough for the owner then it's doing well enough. I don't understand how one can tell someone else that their computer is unacceptably slow for that other individual's personal use.
This is a really unfortunate move by Amazon. My next e-reader will be one that I own (instead of just rent).
Glad that I took the time to jailbreak and pause updates on my 2017 kindle paperwhite while I could.
I'd suggest cheap Android-based Chinese e-Ink e-readers if you want flexibility. My current one is a Bigme B6, which was for sale in my country a few months ago.
Their main advantage is providing access to all e-reading apps available on the Google Play Store, including Amazon's own Kindle app, as well as sideloaded ones such as KOReader.
On the downside, the battery life on those isn't as good as that of dedicated Kindles, Kobos, or other lightweight e-readers, but they still hold a charge for four or five days if one turns off their antennas, which is plenty of time to recharge them.
As for the ebooks themselves, I switched to purchasing from Kobo and other ebook stores. Some sell DRM-less ePubs, which is nice, while those that come with DRM can be easily liberated. And for the occasional Kindle-exclusive that is struck with (temporarily) unbreakable DRM, the Kindle app, although annoying, works well enough.
I'm pleased with OBOOK5. It runs Obook OS which is a Linux OS. Never nagged me to connect to WiFi or anything, I simply plugged a cable to transfer my local stuff.
Same here, I quite enjoy it. Plus there is open source software available, such as crosspoint. It’s easy to flash and an opus call away to change the behavior if you want something to work differently.
Yeah I flashed Crosspoint on it as soon as I got it.. Seeing the improvements it provided was partly what convinced me to buy the device.
I really appreciate that the company that makes the device has embraced the community firmware scene and even links directly to them from their website as a semi-blessed alternative to their official one.
I guess they're very much related, given that they're both tools to flash images to an esp32. But from checking the repo, this seems to be an effort by the tinygo people to extend the embedded go ecosystem.
