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Show HN: GlycemicGPT – Open-source AI-powered diabetes management

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Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#41

Post by pimeys »

nonameiguess wrote: post #33
mhovd wrote: post #7

The risk to benefits ratio of introducing a language model to interpret so clear signals is nowhere near justified. Monitoring and analytics is important, but it is a solved problem. A language model will only be able to hallucinate about the relationship between meals and glycemic response. At best it does no harm, at worst it can directly misinform.

That's just risk/benefit to the user. As the developer, I'd be concerned that publicly distributing and marketing this, even with a GPL "no warranty" license and even free to the user, is illegal.

You can distribute the source code for research purposes legally at least here in Germany. That's how AndroidAPS is even possible.

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#42

Post by nahsra »

M0r13n wrote: post #40

I don't think that LLMs are trustworthy companions in managing a complex metabolic disease like diabetes - especially if you deviate (ever so slightly) from the norm (very lean, very active, strict diet, etc.)! I'm a T1D myself and like to experiment with ChatGPT (or Opus). My experiences are mixed LLMs are overly cautious when it comes to correcting with insulin. They regularly advise against correcting before going…

I understand this instinct, but I can see the appeal of capabilities that are well within the limits of a well-designed agentic system.

Imagine asking such a system, "look at my postprandial response to dosing for the past week and make ratio suggestions for breakfast, lunch, and dinner." This is genuinely helpful, saves time, and well within the reasoning limits. You could spot check if you like.

Is it worth setting up such an assistant for the value you'd get out of it? I guess that's on the user and how many similar use cases exist.

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#43

Post by M0r13n »

tornadofart wrote: post #8

I'm a T1D and tbh it's not that hard to manage, I just wouldn't need that. But for kids or the elderly, I see a use case. The hardest to learn was that an unhealthy lifestyle resulted in a diabetes that was harder to manage. Too much carbs, not enough exercise, etc. After adjusting my lifestyle, it became quite easy. The most pain, in my experience, comes from the discrepancy between the CGM - measured value and the…

> But for kids or the elderly, I see a use case.

These are exactly the kinds of people who should NOT base their diabetes management on the "suggestions" of LLMs! There is the real risk that such users lack the competence or judgement to critically assess the convincing-sounding output of an LLM.

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#44

Post by pimeys »

surgicalcoder wrote: post #23
pimeys wrote: post #13

Earlier quoted context omitted.

I think the only thing that could be made better is tuning the I:C/ISF/Basal values automatically. And ISF is already handled by DynamicISF, while not perfect it reduces the variables you have to tweak. Otherwise, when tuned correctly, oref1 et.al. provide amazing results and are safe. Hard to understand where I would use LLMs in this.

You sort of have that - not automatically though, but you can run autotune against nightscout and get a report of where things need to be adjusted. I run oref1 with DyanmicISF, and just run autotune every few months just to tweak values. I genuinely don't see where I would use an LLM in this process.

I agree. For us, who are really good with software and all that, AndroidAPS is really good when you can easily learn how to use it.

Not that great for non-technical people. But it saved my life at least.

Edit: thanks for reminding me about Autotune. I used the azure app ages ago, but it's now integrated in AndroidAPS behind a secret setting.

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#45

Post by [deleted] »

wg0 wrote: post #21
mhovd wrote: post #7

The risk to benefits ratio of introducing a language model to interpret so clear signals is nowhere near justified. Monitoring and analytics is important, but it is a solved problem. A language model will only be able to hallucinate about the relationship between meals and glycemic response. At best it does no harm, at worst it can directly misinform.

Thanks for calling out! We're even yet debating and trying to understand what impact AI has on software engineering and quality let alone putting AI into something that's directly linked to a human's well being.

[deleted]

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#46

Post by Kryscekk »

As a urologist who built and runs his own clinic management software, I'd encourage thinking about this question early: what does the system do when the LLM refuses to answer, returns malformed JSON, or hallucinates a glycemic value? In medical contexts, a 'silent failure' (system continues despite bad data) is much worse than a noisy failure (system stops and asks the user). The 'happy path' for an LLM-powered medical tool is usually well-designed. The failure paths are where the project lives or dies. Curious how you handle that.

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#47

Post by M0r13n »

nahsra wrote: post #42
M0r13n wrote: post #40

I don't think that LLMs are trustworthy companions in managing a complex metabolic disease like diabetes - especially if you deviate (ever so slightly) from the norm (very lean, very active, strict diet, etc.)! I'm a T1D myself and like to experiment with ChatGPT (or Opus). My experiences are mixed LLMs are overly cautious when it comes to correcting with insulin. They regularly advise against correcting before going…

I understand this instinct, but I can see the appeal of capabilities that are well within the limits of a well-designed agentic system. Imagine asking such a system, "look at my postprandial response to dosing for the past week and make ratio suggestions for breakfast, lunch, and dinner." This is genuinely helpful, saves time, and well within the reasoning limits. You could spot check if you like. Is it worth setting…

> look at my postprandial response to dosing for the past week and make ratio suggestions for breakfast, lunch, and dinner

I'm not so sure about that. A patient absolutely must critically evaluate the LLM's suggestions. A naive user risks severe complications. A user with that kind of competence, however, doesn't need an LLM for such trivial adjustments - they're obvious

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#48

Post by mexicocitinluez »

AnthonBerg wrote: post #34
mexicocitinluez wrote: post #29

Earlier quoted context omitted.

> No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so. You 1000% don't work with the general public in a tech way.

"Baseline adult competence" was load-bearing there. This is not an app for the general public.

> This is not an app for the general public.

Can you point me to where it explicitly says that this app is not to be consumed by the general public? Or explain how that could even be enforced?

And my entire point was "baseline adult competence" means very little. Competent in what? Technology? Insulin administration? Both? If they're competent in technology but not insulin administration, than this is obviously a bad idea. If they're competent in insulin administration, but not technology, then why would they use it?

We're not even at the point where we can definitely say it's a good idea to surface this information to actual professionals let alone someone with no clinical experience.

It's a bad idea, period. I work with both clinicians and the general public and the idea that this can be responsibility used by either is a pipe dream that only people who work with neither can believe in.

Re: Show HN: GlycemicGPT – Open-source AI-powered diabetes management

#49

Post by Diti »

peppetv4 wrote: post #36

Really nice of you to share this, well done! About the risks, managing type 1 diabetes is exhausting, and most people will still sanitycheck the output alongside the hundreds of treatment decisions they make every day. That doesn’t change the fact that tools like this can nudge you to notice and look into patterns or things that needs attention.

Tools like this can also display false low glucose figures, leading you to reduce your slow-acting insulin and skipping fast-acting insulin. A day later, you start feeling nauseous (ketoacidosis), and you’re in danger of death.

I’ll keep using the manual glucose meters (like you advised), and would personally stay the fuck away from any transformer-based LLM to report medical data.

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