Dr. Daniel Knoepflmacher
speaker
173 appearances
1 recordings
1 series
first heard Jun 2026
last heard 23 Jun
Dr. Daniel Knoepflmacher’s voice in public audio — every appearance, attributed to the second.
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you turned to diagnosis in the piece that you wrote in in September of twenty twenty five on AI and diagnosis, you talked about how an L L M, which I think was sounds like was quite surprising to you, really could
take clinical data and that was quite complex and arrive in an accurate diagnosis for the most part in a fraction of the time that would be needed for the top human
Expert.
So that makes us all a little uncomfortable, I think.
The prospect of artificial intelligence doing integral aspects of the work we do that kind of is our pride and our identity is probably, I think, threatening.
And you asked a question, which was the title of your piece: if AI can diagnose patients, what are doctors for?
I'm wondering what's the answer to that question, do you think?
You're speaking about humanism ultimately, which I think is really an important thing that we need to preserve as central to all medical care, to all mental health care right now, because I do think there's some threats to that.
I wanna stick to diagnosis for a moment longer in psychiatry.
Diagnosis relies on interviewing skills, on reviewing history, performing a mental status exam.
And all of this is something which takes a lot of time to master.
I mean, I'm a residency training director.
This is something that we start teaching residents from and in medical students too from the very beginning.
That's very different than work in radiology, which is very important but takes a different kind of pattern recognition, but maybe is something which image analysis through AI could do in a different way.
And I'm just wondering, maybe this is just me being biased from a psychiatric standpoint, but I'm thinking about our incredibly human-driven process of
psychiatric diagnosis, in some ways because we're dealing with a lot more gray than and other medical specialties, if it's going to be a more difficult target for AI integration, and I realize maybe
It's the opposite.
Maybe because there's so much grayness, it's actually a great target because maybe there's new tools like imaging data or voice and facial expression analysis that could be done with AI that we can't really do well as we're listening to somebody's story.
So I'm just curious, based on what you've seen with AI adoption in general.
Do you have an idea of how that could play out, let's say, in psychiatry?
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