[londons_explore]

Statistical diagnoses models have offered similar possibilities in medicine for 50 years. Pretty much, the idea is that you can get a far more accurate diagnosis if you take into account the medical history of everyone else in your family, town, workplace, residence and put all of it into a big statistical model, on top of your symptoms and history.

However, medical secrecy, processes and laws prevent such things, even if they would save lives.

I don't see ChatGPT being any different.

[DrScientist]

This is what effectively doctors do - educated guessing.

In my view, while statistical models would probably be an improvement ( assuming all confounding factors are measured ), the ultimate solution is not to get better at educated guessing, but to remove the guessing completely, with diagnostic tests that measure the relevant bio-medical markers.

[londons_explore]

Good tests < good tests + statistical modelling.

This becomes even more true when you consider there is risk to every test. Some tests have obvious risks (radiation risk from CT scans, chance of damage from spinal fluid tap). Other tests the risk is less obvious (sending you for a blood test and awaiting the results might not be a good idea if that delays treatment for some ailment already pretty certain). In the bigger picture, any test that costs money harms the patient slightly, since someone must pay for the test, and for many the money they spend on extra tests comes out of money they might otherwise spend on gym memberships, better food, or working fewer hours - it is well known that the poor have worse health than the rich.

[DrScientist]

Sure tests cost money - and today there is a funnel pathway - the educated guess is a funnel/filter where the next step which is often a biomedical test/investigation.

But if we are talking about being truly transformative - then a Star-trek tricorder is the ultimate goal, rather than a better version of twenty questions in my view.

So I'm not saying it's not useful, just that it's not the ultimate solution.

[itishappy]

Without a perfect framework for differential diagnosis, this is still educated guessing. In my opinion we're closer to the AI singularity than we are to removing guesswork from the medical field.

[tessierashpool]

this is true, but we're also much closer to Jupiter than we are to Alpha Centauri

[pc_edwin]

"londons_explore" - Ahh the classic British cynicism (Don't ban-ish me señor Dang, I'm British so I can say this).

> Similar possibilities existed in medicine for 50 years

It would've been like building the tower of babel with a bunch of raspbery pi zeros. While theoretically possible, practically impossible and not (just) because of laws, but rather because of structural limitations (vector dbs of the internet solves that)

> Patents and byzantine regulations will stunt its potential

Thats the magic of this technology, its like AWS for highly levered niche intelligence. This arms an entire generation of rebels (entrepreneurs & scientists) to wage a war against big pharma and the FDA.

As an aside, this is why I'm convinced AI & automation will unleash more jobs and productivity like nothing we've seen before. We are at the precipice of a Cambrian explosion! Also why the luddites needs to be shunned.

[londons_explore]

statistical approaches could have been done 50 years ago.

Imagine for example that 'disease books' are published each month with tables of disease probabilities per city, per industry, per workplace, etc. It would also have aggregated stats grouped by by age, gender, religion, wealth, etc.

Your GP would grab the page for the right city, industry, workplace, age, gender etc. That would then be combined with the pages for each of the symptoms you have presented with, and maybe further pages for things from your medical history, and test results.

All the pages would then be added up (perhaps with the use of overlayed cellophane sheets with transparency), and the most likely diseases and treatments read off.

When any disease is then diagnosed and treatment commenced (and found effective or ineffective), your GP would fill in a form to send to a central book-printer to allow next months book edition to be updated with what has just been learned from your case.

[tessierashpool]

> I'm British so I can say this

can you, though? it's not scalably confirmable. what you can say in a British accent to another human person in the physical world is not necessarily what you can say in unaccented text on the internet.

[pc_edwin]

Hahaha nice one.

Funnily enough, it is scalably confirmable. You can feed all my HN comments before chatGPT into well.. chatGPT and ask it whether I'm british based on the writing.

I bet we are just a version or two away from being able fine tune it down to region based on writing. There are so many little things based on whether your from Scotland, Wales or London. Especially London!

[zeofig]

The great thing about AI models is that once you train it, you can pretend the data wasn't illegal

[realPtolemy]

See the glas half full or half empty?

Medical secrecy, processes and laws have indeed prevented SOME things, but a lot of things have gotten significantly better due to enhanced statistical models that have been implemented and widely used in real life scenarios.

[HenryBemis]

To make this feasible (meaning that the TB of data and the huge computing effort is somewhere else, and I only have the mic (smartphone), we need our local agent to send multiple irrelevant queries to the mothership, to hide our true purpose.

Example: my favourite team is X. So if I want to keep it a secret, when I ask for the history of championships of X, I will ask for X. My local agent should ask for 100 teams, get all the data, and then report back for only X. Eventually the mothership will figure out what we like (a large wenn diagram). But this is not in anyone's interest, and thus will not happen.

Also, like this the local agent will be able to learn and remember us, at a cost.

[whimsicalism]

Nonsense.

The medical possibilities that will be unlocked by large generative deep multimodal models are on an entirely different scale from "statistical diagnoses." Imagine feeding in an MRI image, asking if this person has cancer, and then asking the model to point out why it thinks the person has cancer. That will be possible within a few years at most. The regulatory challenges will be surmounted eventually once it becomes exceedingly obvious in other countries how impactful this technology is.

[DrScientist]

But in your scenario - which part is adding the value?

Your deep multimodal models or the MRI imaging?

What you are essentially saying is the signal is so subtle that only a large NN can reliably extract it.

While that may well be the case, it would be better to have a scan/diagnostic that doesn't need that level of signal processing to interpret.

For example - you don't need a large generative deep multimodal model to read a Covid antigen or PCR test.

[whimsicalism]

There are tons & tons of conditions that do not have easy scans/diagnostic and rely on subtle signals - especially if they are not a binary yes/no but a regression style prediction.

We've picked a lot of the low-hanging simple to extract signals, we need large models to go to the next phase for things like parkinsons, etc.

[DrScientist]

I'm not saying there isn't stuff that can't be done more reliably - but I'd argue long term might be better investing in getting better data - rather than better fishing in a pool of low quality data.