[derekdahmer]

I RFTA and the majority of the complaints are about call center metrics and the pressure to ration care. These are real concerns about misuse of metrics, but not AI. The AI empathy thing was a 2024 pilot that was discontinued.

FWIW my wife works for Kaiser and finds a lot of value in the the medical LLM tools available to her. She tells me being able to do live translation, summarize notes, and quickly get comprehensive answers save her time and help her give better care. Her older patients also frequently come in bringing AI-powered alerts from their apple watches that detected cardiac events.

It's annoying that we use broad terms to describe a set of technologies that in some ways can be problematic and in another ways are very beneficial. We gotta evaluate each of these as they come rather than talk about blanket bans.

[isityettime]

Things like this are (sadly) common (and age-old) problems with automation and computerization. (For a vivid account of this phenomenon, check out the novel _Close to the Machine_, by Ellen Ullman.)

As executives and analysts increasingly use the "AI" craze to push automation and computerization (and layoffs) generally, even aside from AI proper, it should not be surprising that the individuals and groups opposing those moves also use the same labels.

The lack of precision in language here sucks. It sucks for the discourse and it also sucks when it comes to focusing anger and productive energy on the core problems (obfuscation of human responsibility, erosion of human agency, declining institutional flexibility, deprofessionalization, etc.). But it doesn't begin with the critics of AI.

[johnnyanmac]

>The lack of precision in language here sucks.

It's a feature. Or at least, a perk. If they want to claim this new shiny rock is AI and people buy it, then of course it's in their best interest to keep the black box mysterious. Being subterfuge for muddying the discourse of critique is just a nice side bonus.

[HappMacDonald]

But it is only a perk for the scam artists who benefit from that.

Yes, it makes sense that the confusion aligns with their interests, and they are unavoidably a big part of the conversation. But it remains a problem for the non-overlapping group of people who actually value the social contract, and for us finding a solution which helps take one more step to defeat the scammers remains valuable.

[worik]

Yes. It is useful for the scam artists:

X, Open AI and Anthropic, to name three

[lazide]

Near as I can tell, like crypto, 90% of the ‘discussion’ has been taken over by scam artists, and any folks trying to have a non-scam discussion get yelled at by everyone else.

[delusional]

I am very interested in your reading of Close to the Machine. I read it myself a couple of years ago and found it a wonderful telling of the early days of tech, with overtones of the "technology workplace" that were still very true to this day. I did not pick up on any commentary on automation or computerization, outside of the general critique of bureaucratic systems that alienate you from the outcomes of your labor.

Do you have anything I could read to understand your reading better? I would love to be able to dive back into one of my favorite books with a new lead.

[isityettime]

It's in the opening chapter, when the narrator talks about her experience working on a software system for AIDS patients and their medical care providers. The critique is not just of bureaucracy per se— the same bureaucracy, institutionally, exists before and after the implementation of the software system. The difference is that the informal judgment of caseworkers is converted from something fuzzy and flexible into fixed fields, permissions, enumerable and particular relationships, etc., and ultimately results in fewer people being able to access those resources they need.

I remember the novel relating similar themes more generally, which has shaped how I even think of the title after reading the book. Being "closer to the machine" isn't just about working at a lower level; it's about the way working with computer systems reshapes the people developing for them and the people using them. It's about how even "cushy" software companies lean into, hire for, and draw out obsessive, dissociative traits: the included lunches, the laptop you can bring home and continue to work on, the places to nap or crash on-campus.

Maybe a very dark reading, and certainly colored by some of my own experiences in tech and where I was at in my career when I read it. But for me there's absolutely this sense that the need and desire to be "close to the machine" is also something that lures us away from ourselves and from each other.

[randysalami]

To your penultimate part, I remember when I was younger, I always wanted to be a software engineer. Those obsessive, dissociative traits I cultivated because they seemed so welcome at the places which young me wanted to work (Google, Facebook mainly). And those places seemed so right (and paid so well). As I got older, I realized there was way more nuance. What I once thought were strictly virtues to be lauded… had more gray than I imagined. I don’t think a lot of people in our field have or will have that wake-up.

And I think this is a big part of the problem. Being a shell of a human does not lend yourself well to empathy and solidifies your ego and that you are “worth” more than others, reinforced with a lucrative salary, praise, and those perks you mention.

[obscurette]

Being close relative for several med and care workers we have discussed it a lot and consensus is that it really depends. For example relying on LLM summaries sounds great until it doesn't. It doesn't matter whether you misunderstand LLM summary or LLM "misunderstands" you – there are real risks involved, and you wouldn't want them to weigh on your conscience if they were to materialize.

Relying on LLM to summarize things for you has one more issue. To outsiders, this seems like a tedious process, but is actually very important part of the thought process. Wording your thoughts and writing these down helps people to discover new aspects of the problem. It's how people learn.

At the moment consensus is that it must not be banned, but also not mandated in any way - people must take responsibility, and they must be able to decide for themselves where and when the LLM use is justified and where it is not.

[adrianN]

I don’t think that it is possible to both allow the use of LLM and not mandate them in modern metric driven work places. Either you ban them or you force people to use them for game theoretic reasons: they are slower than their peers and quality of the work is harder to measure than quantity. All you achieve is shifting the blame to the employees if the LLM messes up. Come to think of it, that probably is a highly desirable outcome for the decision makers, so perhaps that will actually be the policy that becomes universally adopted.

[KronisLV]

> I don’t think that it is possible to both allow the use of LLM and not mandate them in modern metric driven work places.

What I’d personally be most concerned about would be the risk of bad models instead of SOTA being used which would be especially error and hallucination prone.

If you’re gonna do it, do it right. Otherwise don’t bother at all.

[squigz]

> All you achieve is shifting the blame to the employees if the LLM messes up.

How is this not a good thing for everyone?

[grayhatter]

> It doesn't matter whether you misunderstand LLM summary or LLM "misunderstands" you – there are real risks involved, and you wouldn't want them to weigh on your conscience if they were to materialize.

"fun" fact, a number (some? many? all?) of the LLM speech to text models will editorialize or hallucinate words for you, to make your speech fit the pattern it expects.

I don't think it's appropriate to use a model that can editorialize in a medical transcription setting. I feel that crosses a huge number of ethical lines.

> At the moment consensus is that it must not be banned, but also not mandated in any way - people must take responsibility, and they must be able to decide for themselves where and when the LLM use is justified and where it is not.

I have a really hard time understanding how supposed experts in their field, have been taken in so perfectly and so completely. You're a trained and certified medical/healthcare worker, responsible for the health, safety and well being of other humans, and yet willingly turn over parts of the process that exists to protect the safety and health of others, to a machine that you know can and will make mistakes you wouldn't or even couldn't ever reasonably make. You're supposed to make sure it works and is safe for everyone you're responsible for before you start using it... but then, I prefer healthcare that doesn't move fast, and break humans.

I'm sure someone will try to play devil's advocate and suggest it's a good thing to ration care, and that you have to acknowledge that you can't save everybody, faster is better after all But no, I'm angry enough about it that if a licensed person suggests that, I'll gladly complain to your licensing board (rhetorically speaking, I'm not gonna try to doxx anyone). You can't ethically argue for rationing care, and letting a machine known to make errors in ethics or care decisions, that you have been trained not to make. (Advocating for a reduction in the standard of care in order to increase profits.) (Yes, you're correct, if you're in a state of triage, you're required to, and in those cases, using an imperfect machine is possibly preferable, and probably ethical, but still objectionable. I currently refuse to believe that the US is in a state of emergency, but perhaps that's where I'm wrong) Part of your licensure you promised to prioritize patient care above personal gain. You can't then make decisions to experiment with systems you cant prove are safe.

I can understand in when SWEs ignore the best interests of the humans they're working for. But Doctors and Nurses? I guess I expected better from my former colleagues?

[nradov]

In a medical setting we first have to be clear about whether the LLM is acting as a transcriptionist or a scribe. Those are different roles. A transcriptionist merely writes down everything the clinician says verbatim, with perhaps a bit of formatting to fit a standard template. Speech recognition software has been used for transcription since before LLMs even existed. Sometimes a human transcriptionist will review and edit the software output. But ultimately the clinician is accountable for approving the document before it legally becomes part of the patient chart.

A medical scribe isn't just taking dictation, they're doing some level of interpretation and often entering structured, coded data directly into an EHR. This is a more complex role and requires some clinical skills. There are some new LLM products that automate this to an extent, but ultimately the licensed clinician is still legally accountable for what goes into the patient chart.

I can absolutely ethically argue for rationing care. All healthcare systems ration care although the means vary. Demand is effectively infinite, especially from older patients with complex or terminal conditions. Resources are finite.

[giardini]

I saw someone whose doctors were misled by this "transcriptionist vs scribe" difference in an ER situation. The LLM was listening and recording as the doctor asked questions and the patient answered. PT had suffered a falling episode. ER doctor asked if pt had any warning (e.g., was dizzy, faint, or unbalanced before fall). Pt said no. Later when reading final discharge report with pt we noticed that the LLM had inserted that the pt "experienced dizziness and then fell" which completely contradicted the pt's actual words.

Since then pt has had to re-explain to multiple doctors how the ER report was incorrect, to their usual MD skepticism. So, for now, I figure LLMs are for shite. Maybe in another 15 years...

And it pays to read everything on a medical report before leaving the facility.

[phoghed]

> I have a really hard time understanding how supposed experts in their field, have been taken in so perfectly and so completely.

Yes, surely it’s not you. It’s all the experts that are wrong and being hoodwinked. Why can’t they just see what you can?

[grayhatter]

> Why can’t they just see what you can?

Most humans use a default trust model for life; they've been lied to. That's part of why I'm angry, if you trust other people; why would you look for it? This entire article is about the fallout from not looking for it, and the resulting harms to pt care. So, it turns out, it was a mistake to adopt the new tech quickly; you know, as evidenced by the harms to patients?

So I don't know, why do you think they couldn't they predict the harms that I thought were obviously predictable, and then happened?

[red75prime]

> where and when the LLM use is justified and where it is not

while being bombarded with articles like "AI makes things worse", "AI consumes all the water" and the like

[fn-mote]

> AI-powered alerts from their apple watches that detected cardiac events

Surely these are “good old-fashioned AI” (statistical learning) and not LLM, though.

I just want to be clear that the “medical LLM” tools are the new ones, and the Apple Watch alerts aren’t.

[bitwize]

LLMs are statistical learning. GOFAI is symbolic, rules-based stuff, expert systems and that.

[kennywinker]

There is still a categorical difference between how they are being used. Specifically analytic vs generative. Generative AI (LLMs and image generators) are the ones people have issues with - pretty much nobody cares about ML processing for analysis.

[woodson]

There’s a bit of a grey area, for example speech recognition. Would you classify that as analytic or generative? Whisper and speech LLMs work pretty well, but can completely make up stuff that wasn’t in the audio at all (see e.g. “thank you for watching” transcribed during silence). Other approaches are closer to the acoustic evidence but may make other mistakes (especially wrongly transcribing long tail, low frequency terms). Pick your poison.

[kennywinker]

Analytic, but badly made.

[tyfon]

> pretty much nobody cares about ML processing for analysis.

I work in a bank and a can tell you that the customers absolutely hate ML when it rejects their loan application. Over the pond in the US, I have an impression that the fico score is not exactly popular either, but I have no first hand experience.

[disgruntledphd2]

As long as you can get a reason from the model, it's not that bad.

Black box automatic decision making is much more problematic.

[nradov]

In the US, FICO scores are mainly unpopular with "credit criminals" who have low scores. The score performs quite well in predicting how likely a borrower is to repay a loan. The problems that arise with credit scores in general are when they're used for purposes they were never designed to serve, like screening job applicants.

For home mortgage lending, FICO scores are now being partially replaced by VantageScore.

https://vantagescore.com/

[kennywinker]

People have absolutely hated their loan applications being rejected since before ML was being used anywhere near it.

As evidence, let me cite the “computer says no.” sketch from 2004

[bitwize]

In 2004, the logic driving such decisions was literally GOFAI: rules engines that fire off responses when certain criteria are met.

[AussieWog93]

Who downvoted this person for correctly defining GOFAI on an tech forum?

[thatfrenchguy]

Cardiac events from Apple Watches is not “AI” though

[jonahx]

It unequivocally is AI. It's just not LLM-powered.

The rising LLM = AI equivalency is unfortunate.

[LoganDark]

It's machine learning, which has overlap with AI but is not completely equivalent.

[granzymes]

The “overlap” is that all machine learning is AI, but not all AI is machine learning.

[LoganDark]

There are both ML that is not AI, and AI that is not ML.

For example, if you pick them manually, decision trees can be AI but not ML. Video game character behavior is a trivial example.

Eliza for example is also not ML, but could be called AI.

Likewise, there is ML that is not AI. Such is debatable, because you could always argue that using machine-learning on anything results in intelligence. The way I see it, things like image enhancement or voice replacement are not artificial intelligence at all. I probably could not define a hard line where it becomes artificial intelligence though.

[fragmede]

To me they're the same thing. If there's a bunch of training data that is fed into a system that creates a model, then it's not traditional programming, where someone laboriously writes out if statements by hand. AI and ML aren't, as far as I'm aware, rigorously specifically defined terms. They're words that marketing picked up and ran with it. To me, what matters is: is there a black box somewhere in the system that's a bag of numbers, or is it code that a human could dig in and read.

[AdieuToLogic]

> The “overlap” is that all machine learning is AI ...

"All machine learning" is not AI, as k-means clustering and linear regression, amongst others, are very much ML without qualifying as AI algorithms.

[ruszki]

https://en.wikipedia.org/wiki/Artificial_intelligence

As it is taught literally every single AI/machine learning course on the world, machine learning is very much part of AI completely since inception.

I don’t completely understand why it is this important for you to argue against this completely defined fact.

[solumunus]

The machine is learning something so that it can produce outputs based on its learned knowledge. At a high level that seems to be very clearly AI. What am I missing here? You’re probably right, I’m asking genuinely.

[disgruntledphd2]

Technically linear regression is statistics rather than ML, but I feel like the GNU/Linux people whenever I point that out.

[lazide]

Bayes is turning in his grave fast enough to power Manhattan.

[HappMacDonald]

> but not all AI is machine learning

I will instead pick at this latter part of your claim. What is an example of something that is AI but that is not ML..?

[lukan]

A chess engine.

[kennywinker]

At this point AI is a marketing term not an actual category

[thorbutt]

See: Samsung selling "AI" vacuum cleaners and washing machines

[pj_mukh]

AI and ML have very clear definitions[1]. ML has been a subset of AI, always has been. Latest marketing or "scare quotes" doesn't/shouldn't change that. Especially not in a technical forum like HN.

[1]: https://en.wikipedia.org/wiki/Artificial_intelligence

Ctrl-F Machine Learning. Apple Watch alerts are Machine Learning

[ch4s3]

It’s machine learning, which people routinely called AI not so long ago.

[nunez]

ML was always marketed separately as AI/ML, with AI being things like CNNs/RNNs/BERTs and such. Always felt like a distinction without a difference.

[andor]

Laypeople are changing how “AI” is used in common language, like they previously did for “algorithm” and “crypto”.

The textbook definition of AI is a system that solves problems that are difficult for humans. Whether the approach uses formal logic, machine learning, neural networks as a special case of machine learning, optimization, search problems, etc. does not matter.

[BeetleB]

I don't think so. ML was always associated with AI. When it wasn't, it was called statistics.

[LoganDark]

I never heard people calling machine learning "AI" until large language models made it trivial to market it as such. Like, I remember back when Netflix, for instance, was going around advertising how machine learning (not AI) powers their recommendations.

[inopinatus]

> I never heard…

You should listen better. The University of Edinburgh had an entire Department of Artificial Intelligence when I was an undergrad there in the 1990s, and one of the things it researched was machine learning.

[LoganDark]

I don't see how including machine learning under the artificial intelligence umbrella counts as calling machine learning AI.

[inopinatus]

My local supermarket places the almond milk in the dairy section, and some people find this very upsetting.

[fn-mote]

Ed: I disagree. My recollection is that machine learning was routinely sold as “AI” even when it obviously wasn’t. (IBM’s Watson was good at Jeopardy but not real medical applications.)

This isn’t exactly the same, but nothing in the book Paradigms of Artificial Intelligence would be considered AI today.

[LoganDark]

You must be thinking of a different machine learning. All the on-device machine learning, backend machine learning, OCR, etc. was all called "machine learning" before LLMs. Yes, the field of artificial intelligence still existed, often used machine learning, and called the result "AI". But Apple would call keyboard prediction machine learning. Microsoft would call OCR machine learning. YouTube called machine transcription machine learning. Google called camera image enhancement machine learning.

Microsoft now calls everything AI (actually mostly "Copilot"). YouTube now calls everything AI (including genuine LLMs and generative features, but also everything it used to call machine learning). Google now calls everything AI (including everything it used to call machine learning). Apple is seemingly the only one immune.

My argument is not that no one ever used "AI" to refer to a product that utilized machine learning, but rather that the term of art in the industry for machine learning itself was actually "machine learning", not "AI", until LLMs took over and made it "AI".

You would not pull a library off the shelf for "AI", it would be for machine learning. You would not implement and perform "AI", but machine learning. Even central parts of the AI ecosystem like PyTorch advertise as being for "deep learning", which is a subset of machine learning. Not "AI".

[awwaiid]

Counter example, the book that is the foundation of much coursework and learning for people in AI, has a whole section on "Machine Learning" with all that k-means and such in there - https://aima.cs.berkeley.edu/

[techpression]

Thank you. I was starting to think the history revision was almost true, but your recollection is very much in sync with my own. Everything was machine learning, nobody talked about AI unless it was for research, now marketing has changed that, unfortunately.

[woodson]

For a long time, AI was a bad word that stood for unfulfilled promise. See AI Winter. Hence, researchers strictly avoided the term while still working on learning algorithms, the same that power LLM training.

[budsniffer952]

Yes, because we had the same stupid arguments then: "that's not AI!"

So machine learning became the marketing.

[LoganDark]

What makes them stupid?

[fragmede]

It's stupid because there's no formal definition of what counts as AI, so we repeat the same conversation every time. Hence the joke ML is just AI that works.

There's a book from 1995, called Artificial Intelligence: A Modern Approach, by by Stuart Russell and Peter Norvig, which gives the definition "AI is the study of agents that receive percepts from the environment and perform actions." but doesn't define intelligence.

https://aima.cs.berkeley.edu/2nd-ed/preface.html

If we go with that, though, I'd say AI is anything that uses a model to make decisions instead of hand written "if" statements.

[bananaflag]

In 2011, I took an AI course at my university and it was all perceptrons and neural networks.

[bagels]

I took one longer ago than that and it wasn't all perceptrons and neural networks. It included other things too, like: planning, search methods, inference engines, decision trees, ...

[bananaflag]

Yeah, in my university there were two AI professors, one did neural networks, and one did search stuff, and it depended on whom you are assigned to.

[eru]

It would have been, 20 years ago.

[granzymes]

We call things AI until they start working. See also: robots (your washing machine is a robot, but it works so you don’t think of it that way).

[Dylan16807]

Calling things "robots" is more about the amount of movement. Spinning in place like a washing machine sprayer isn't enough to qualify.

A paint conveyer belt is not a robot. A sprinkler system is not a robot. A CnC machine might be a robot. A conveyer belt that sorts items might be a robot. A roomba is a robot. And all of these function just fine.

[kennywinker]

I think of robots as general purpose, machines are specific purpose. When it works, we make it single purpose because that’s far far cheaper than general purpose.

[eru]

Are welding machines at the Volkswagen factory robots?

[kennywinker]

Idk could you swap out an attachment and make them to something completely different?

[AussieWog93]

Are these the ones with 5+ axis arms? Yes, they're robots

[AussieWog93]

When I was studying ML back in 2017 people were still calling things like image classifiers "AI".

[AdieuToLogic]

>> Cardiac events from Apple Watches is not “AI” though

> It would have been, 20 years ago.

No, it would have been called what it is both then and now; an asynchronous message emitted by a device having sensors capable of detecting when to do so.

[tclancy]

Just for clarification, is your wife a doctor or a nurse?

[derekdahmer]

Physician Assistant, which is like a RN

[ajb]

I understand the frustration, but it's justifiable for the public to be concerned about AI in general because the novelty of the technology means that boundaries between beneficial and problematic usage is not yet stable and well defined. For existing technologies consensus on that has usually been reached, although it changes over time. In many cases we may not yet have enough information to decide.

Nor is this a trivial decision. AI has the potential to change society and economic relations as profoundly as the industrial revolution, or the invention of the printing press.

These boundaries are also contested, as interests which benefit from a particular application are different from those whose interests are harmed. Society needs to identify which usages have a net benefit.

It also needs to define which usages cause "absolute" harms which is will consider unacceptable regardless of benefits to some parties. Such as, potentially, reductions in personal autonomy, increased leverage or dominance by government or private interests.

Not only that, but data and models which were collected/ built for one purpose can easily be adapted for others.

This is also basically now happening all at once in many domains.

In short, you can expect there to be tension over these boundaries for some time. It's not realistic to expect that others will agree with your personal perception of which applications are "obviously" unproblematic.

[truncate]

>> It's annoying that we use broad terms to describe a set of technologies that in some ways can be problematic and in another ways are very beneficial. We gotta evaluate each of these as they come rather than talk about blanket bans.

I totally get it. I think few years, if some company said they record and transcribe every meeting/interview they take, it would be concerning. Now, its somewhat a norm for people to use these AI meeting tools which record everything you say and then go back to recording and exactly what people said. I'd call it surveillance than AI

[reinitctxoffset]

“There is always a point at which the Amodei ceases to manipulate the media gestalt. A point at which the capital misallocation may well escalate, but beyond which the Amodei has become symptomatic of the capital markets themselves. Amodei as we ordinarily understand it is innately media-related. The Hangzhou hackers differ from other AI engineers precisely in their degree of self-consciousness, in their awareness of the extent to which media divorce the act of giving Amodei fuckin money from the original sociopolitical intent.”

[Quarrelsome]

everyone is very thirsty for AI hate, so its not unexpected. Today its a mixed bag of corpo hate, anxiety about the future, inequality and traditional class warfare, combined with the typical technical ignorance.

I would expect companies to blend shit metrics with AI systems, if not at Kaiser then at other places. People lack imagination and using AI to monitor your workforce has to be one of the possibly worst ways to use it. Alternatively some dickhead will "lean startup" their way into measuring "performance" in such a way with the "help" of AI that they will do something even worse.

[vkou]

> Today its a mixed bag of corpo hate, anxiety about the future, inequality and traditional class warfare, combined with the typical technical ignorance.

Are there no actual problems with what it promises/how we're expected to use it at work/how it is used against us by people who use it at work? Is it just anxieties and feelings, that aren't actually grounded in anything? Everything's getting better for everyone, no downsides?

[HDThoreaun]

In this case it seems to me that LLMs in medicine pretty much are just better for everyone with no real downsides. Im not a doctor but have 2 in my family I speak with regularly and from how they talk about it care is pretty much just unequivocally better now because they dont have to be sitting at the computer taking notes all the time. It removed a huge part of the process that neither patients nor doctors enjoyed.

[vkou]

Are prices going down, as they need to do less work per patient?

Or are the providers just pocketing the difference?

Given that prices are definitely not going down, I'm not sure how this makes my life any better.

[HDThoreaun]

> I'm not sure how this makes my life any better.

Well instead of staring at a computer the whole time now they can actually make eye contact with you. IMO that alone makes my life quite a bit better

[Quarrelsome]

Nobody knows what's actually going to happen, so we assume the worst. It's like how we like to shit on Amazon without appreciating how some people have benefitted from being able to side-step the gatekeepers of old by being able to go straight to market themselves without needing bricks and mortar. Humanity, fear and distrust are best of friends. That's what I'm calling out, I have no interest in playing Nostradamus.

I think OP is a good case in point where people are blaming AI but really the issue here is the human in the loop, the person implementing these abhorrent policies that happen to be powered by AI.

[vkou]

> I think OP is a good case in point where people are blaming AI but really the issue here is the human in the loop, the person implementing these abhorrent policies that happen to be powered by AI.

I don't care if the torture nexus itself is not at fault, now that it has been created, it has enabled its owner to do a bunch of novel bad things, and it deserves a large part of the blame.

[ai_fry_ur_brain]

I personally will not use a provider who uses llm tools. I know it makes me and my coworkers less careful and lazier. Qualities I dont want in a health care providor.

Ive actually moved primary care physicians over this once already, found the oldest guy I could who barely knows how to use a laptop but spends a bunch of extra time with me.

[flir]

Funny how we assess risk. Not criticising you - I'm as irrational as the next guy - but "I found the oldest doctor I could" seems like it has a different set of risks baked in.

[iknowstuff]

The older the doctor the more experience they have, but also their knowledge is more outdated (how many keep up with medical journals?) and their brain is worse at learning and connecting dots so I'm not sure I'd choose the oldest I could find.

[SoftTalker]

The human body is the same as it was before your doctor was born.

[pbhjpbhj]

Medicine however is not the same. Nor is our understanding of the human body.

For example, a 60 yo doctor would have been born before the first heart transplant, the recipient lasted 18 days. Now 5000 are performed each year and after 5 years 80% of recipients are alive.

[BeetleB]

The Earth was mostly the same in the 1300's. Let's use material from that time to do engineering.

[HDThoreaun]

Our knowledge of it very much is not

[bevr1337]

My current provider shows me their computer monitor for the entire appointment. I didn’t ask for that treatment but I really appreciate it.

[fn-mote]

When LLMs stop helping me with my work, I’ll stop going to doctors who use them.

Too bad for me, it’s a real mixed bag. I need a doctor who is an AI-using LLM skeptic, I guess.

[BeetleB]

This translates to "I'd rather get the traditional types of medical errors instead of the modern types of medical errors."

Better the devil you know ...

[isatty]

Likewise. I basically

- will not see a “provider”. A doctor (MD/DO) only please.

- will not see a doctor who uses LLM tools (or therapists for that matter)

- Make it abundantly clear that if you do then we’re not a match.

- Will pay significantly more/go out of my way for for this.

[nradov]

Regardless of whether your preferences are rational or not, you're quite lucky to be able to pick and choose. There is a shortage of clinicians and it's only going to get worse so most patients will have to take whomever they can get, especially if they're not able to wait months for an appointment.

[isatty]

Indeed, I’m definitely in a privileged position.

[BeetleB]

In a similar vein: Will not go to a doctor who is a DO and not an MD.

We all have our biases.

[api]

“AI” in title gets clicks. So “AI” must be in title.

[nerevarthelame]

AI is directly mentioned in 24 of 53 paragraphs from the article.

[FloatArtifact]

Consider ambient listening in hospitals. Encounters are recorded then AI creates a summary. Those audio recordings then can be used for any other purpose. Consider the ramifications, A panopticon of metrics derived from AI. Imagine a AI used as a review tool across all ecounters a nurse performed in a year. IA will transform how data is stored in healthcare. It's going to move towards the data lake model storing information in its raw form for post analysis.

[worik]

What horrific distopian future you imagine