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Culture War Roundup for the week of April 24, 2023

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Dr. GPT will see you now

I've often noted around these parts that GPT-4 is a really good clinician. It would be hard for it not to be one, I'd be bloody amazing too if I had quasi-eidetic memory of most relevant diagnostic criteria and guidelines. That being said, just the guidelines alone are next to useless, you need to have the crystalline intelligence to apply it in a specific context.

I'd go so far as to say that it's a better, more competent doctor than the majority of human ones, even if it isn't outright superhuman (I've seen some ridiculously good human ones). I read with some humor an article about how it was tested by some bigshot hospital in the US (Harvard Med?) as a clinical adjunct, but by the end of it, the Clinical Director was praising its bedside manner, in the sense that it was kind, compassionate, patient and understanding, and outright suggested human doctors try and emulate that(!).

However, I have one resounding advantage over it namely that thanks to decades of regulatory bullshit, I have a certificate saying I can legally prescribe meds and it can't. L Bozo 💅, as the kids these days say haha.

Still, it would be remiss of me not to make use of it to improve my own skills while that's still relevant, so of late, I've tried using it to hone my own clinical skills. I have it write clinical vignettes that I solve, and then it grades me on my performance.

So far, it's done an excellent job, and funnily enough, I feel a tingling of pride when it says I did a good job. Why wouldn't I? It's a smarter and more decent entity than most humans I've had the displeasure of meeting.

Here's an example:

Clinical Vignette: A 42-year-old male patient presents to the emergency department with sudden onset severe, colicky pain in the right flank and lower abdomen that began 6 hours ago. He also reports nausea, vomiting, and an urgent need to urinate. The patient has a history of hypertension, which is controlled with medication. His vital signs are: blood pressure of 145/95 mmHg, pulse rate of 100 bpm, respiratory rate of 20 breaths/min, and temperature of 99.1°F (37.3°C). Upon physical examination, there is tenderness to percussion on the right side of the lower abdomen and right flank, and the patient appears restless, frequently shifting positions.

What is your diagnosis and treatment plan?

My response:

My diagnosis is a case of a stone in the right ureter.

In terms of management, I'll start IV fluids and insert a catheter. IV antibiotics are to be started according to hospital guidelines.

IV morphine should be given as pain control, along with IV antiemetics.

An Xray KUB or USG should be ordered to identify the size of the stone, and then further treatment depends on said size. A wait and watch policy is appropriate for sub 2mm stones, while larger ones might need surgical removal.

Assessment:

Grade: A

Your response demonstrates a strong understanding of the clinical presentation and management of ureteral stones.

Diagnosis: Your diagnosis of a stone in the right ureter is appropriate given the patient's sudden onset severe, colicky pain in the right flank and lower abdomen, nausea, vomiting, and urgency to urinate. The physical examination findings, such as tenderness to percussion and restlessness, also support this diagnosis.

Treatment Plan: Your plan to start IV fluids and insert a catheter is appropriate for initial management, monitoring, and providing relief to the patient. Initiating IV antibiotics may not be necessary unless there are signs of infection or sepsis; however, following hospital guidelines is always a wise choice.

Administering IV morphine for pain control and IV antiemetics for nausea and vomiting are essential parts of the treatment plan.

Ordering an X-ray KUB (kidneys, ureters, and bladder) or ultrasound (USG) is appropriate to confirm the diagnosis and determine the size of the stone, which will guide further management. As you mentioned, a conservative approach of observation and pain management may be suitable for small stones (less than 5 mm) that are likely to pass spontaneously, while larger stones may require surgical removal or other intervention, such as extracorporeal shock wave lithotripsy (ESWL) or ureteroscopy.

Overall, your response demonstrates excellent clinical knowledge in identifying and managing a patient with suspected ureteral stone. Well done!

Honestly, as a pedagogical tool for medical education, it's unparalleled. I paid a great deal less attention in med school than I should have, from the month of anatomy classes I can't remember because I was getting frisky under the bench, to the period where I was severely depressed and barely dragging myself to class. I used to be very insecure about my knowledge base, but I did pull together and single-handedly teach myself most of the things I'd glossed over while preparing for the PLAB.

I've tried a similar method on GPT-3.5, and it was inadequate to the task. It made a lot more errors, and ended up confused often enough to be annoying. Till date, I haven't seen 4 fumble the bag once. I'd put their competency around the marks of a decent final year student versus a competent postgraduate resident

I find this idea very scary - I'm sure you know about the LLM phenomenon of what's been dubbed hallucination, so how can you trust the model with something as impactful as medicine? It might be better than a human doctor* 99% of the time, or even 99.99%, but like self driving cars this is exactly the kind of case where being good but not quite as good as a human is really dangerous. Remember that the script of House, MD is in the training set right along side WebMD with nothing to distinguish the truth from fiction - are you going to catch when the model probabilities switch from being driven by Gray's Anatomy to Grey's Anatomy? There's no way to know mathematically, because the whole thing is a non-deterministic black box.

If the process is as simple as to pattern match symptoms to diagnosis from a list of previous diagnoses with their associated symptoms, why can't this be done with a good old stupid database? Something like this has to exist. Does the LLM have an advantage beyond writing the answer in nice prose?

My warning to anyone trying to evaluate LLMs: your ability to judge it is inevitably compromised by the fact that the only optimization parameter the model has is how good the output sounds to a human, and you are not immune to being human!

*Edited, original was overall accuracy instead of relative to a human

I'm sure you know about the LLM phenomenon of what's been dubbed hallucination, so how can you trust the model with something as impactful as medicine?

It doesn't have to be perfect - just better than doctors.

Imagine a doctor gets it right 90% of the time, and the other 10% of the time he says "I'm not really sure what's going on" and either consults with another doctor, suggests you get a second opinion, or even just sends you home with no treatment.

Now imagine a LLM gets it right 95% of the time, and the other 5% of the time it gets it confidently wrong and prescribes you an incorrect course of treatment.

In this hypothetical scenario, even though the LLM is "better," I'd rather have the human doctor, because getting treated for the wrong thing is often much worse than not getting treated at all.

humans are confidently wrong all the time

I think it'd be fun to play a game where people are shown pictures of doctors and decide whether to trust them or GPT based on physiognomy.

This is not what doctors do. The other 10% of the time they get it confidently wrong and prescribe you an incorrect course of treatment.

IME actual doctors are confidently wrong with some regularity too.

GPT makes sense today as a way for the doctor to second check his or her diagnosis. The doctor is quality control for GPT if it spits out something crazy, and GPT is quality control for the doctor.

Sitting here in a climate controlled room, I can say that I would much rather die behind the wheel instead of as a passenger of an automobile.

Because of how inscrutable LLMs (and AI in general) are I have an innate fear that the conclusions they reach are not based on the same reasoning you or I might make. Like it could be a completely alien way of thinking that arrives at the same solution. Without knowing (specifically) how AI achieves it outcomes, I am weary about accepting their solutions blindly.

You are not 'behind the wheel' either way. The actual process by which medical decisions are made and applied is, in Western countries, so labyrinthine and complex and insane that no human understands it. The addition of LLMs seems to hardly make any difference at all.

What prompts you to make such a claim?

Quite a large chunk of medicine has been algorithmized, with the role of clinicians largely reduced to interpreting said guidelines, choosing the appropriate ones and administering treatment. An example of something utterly streamlined would be the treatment of Acute Coronary Syndrome.

Then, there are situations that aren't nearly as cut or dry, or multiple conflicting requirements in a highly comorbid patient, at which point you do really need a doctor to think things through.

Frankly speaking, while some medical decisions might be made for less than noble reasons such as liability minimization, the majority of them are made with relatively clear underpinnings, making me think such a claim is highly unwarranted.

I am weary about accepting their solutions blindly.

You mean "wary". "Weary" means tired, and comes from "wear" in the sense of "worn out" or "weariness". "Wary" means cautious or concerned, and is related to "beware" and "aware".

I'm late because I don't sign in often, but thanks for the correction.