As explained to me, the notes are verbose and do not focus on actionable parts of the consultation. Doctors often have to add a lot of missing detail for legal reasons and if they had to tweak the output through multiple LLMs they would have even less time for their consults.
Not for code, but my wife is a doctor and has gone from using AI scribes back to manually typing out notes and has multiple friends who've done the same. Turns out the gains were eroded by having to check the work and turn verbose prose into an actual note.
Feels like Fable's edge ended up just being long horizon task scaling, which post-training seems to achieve as seen here. Wonder what the next frontier is? Improvement in specialised tasks or computer use?
Just a small meta note: most of the comments in this thread appear to be posting their own codebase (typically AI-generated) that accomplishes the same goal. It's interesting that this problem is simultaneously in high demand and yet considered trivial enough to vibe code per-user solutions to it.
Variations of this comment have been posted for over a year. The pelican has now morphed into part of HN culture rather than a legitimate benchmark, but it's still valuable as a meme.
This is terrifying, but I couldn't help myself from frustration at the LLM writing that only worsened over the course of the post. Bloggers, it's not subtle. Please, stop, or at least disclose it.
A pretty insightful viewpoint I heard recently from a doctor friend: doctors and hospitals believe that only a corporation could possibly implement this, so they fall into the SaaS trap and lose data sovereignty.
Under the hood, a lot of the companies are Llama or Gemma wrappers connected to whisper.
There's a lot of people in this thread that don't seem to have caught up with the fact that AMD has worked very hard on their cuda translation layer and for the most part it just works now, you can build cuda projects on amd just fine on modern hardware/software.
Those efforts were for diseases without treatment. But, prolactinomas already have treatment (surgery plus medication). I always ask myself what is the desired outcome from research effort - in this case it seems to be an attempt to cure/totally eradicate a prolactinoma, which would require hundreds of millions of dollars and custom medication (probably immune checkpoint inhibitors) to do, and this disease already has a treatment available.
I'm confused by this post because it seems like his partner got best therapy possible with two surgeries followed by medications without going blind or having other major hormonal issues which can happen after surgery. As correctly stated in this thread prolactinomas aren't a death sentence or even (technically) a brain tumour, and the major risks have been avoided so far. What exactly is being accomplished by a VC deeply researching this case beyond satisfying the valid desire to help your life partner?
There are actually a lot of freely available medical articles on PubMed. Agree about the proliferation of lower quality journals and articles necessitating manual restrictions on citations.
It's crazy to me that somewhere along the way we lost physical media as a reference point. Journals and YouTube can be good sources of information, but unless heavily confined to high quality information current AI is not able to judge citation quality to come up with good recommendations. The synthesis of real world medical experience is often collated in medical textbooks and yet AI doesn't cite them nearly as much as it should.
I think this summary is reductive, because it ignores the surprisingly dense layers of middle management in hospitals and clinics that are paid more than the medical professionals (and even that ignores external middle managers like PBMs etc).