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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

#21

Post by wg0 »

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.

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

#22

Post by pu_pe »

AnthonBerg wrote: post #17
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.

My experience is completely the opposite, of using LLMs to pattern match and cast diagnostic nets. Is your perspective based on, say, opinionated principle?, or experience? The benefits are enormous. The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.

Risks:

Changing parameters on the insulin pump because the LLM said so

Neglecting to seek actual medical advice believing a LLM replaces it

Misunderstanding medical complexity (ie a prescription due to medical history not available to the LLM)

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

#23

Post by surgicalcoder »

pimeys wrote: post #13
surgicalcoder wrote: post #3

I'm a T1D who has an insulin pump looping with AndroidAPS and NightScout, what does this give you that Nightscout and Autotune doesn't give you? And how do you deal with AI hallucinations?

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.

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

#25

Post by hombre_fatal »

vsaravind007 wrote: post #19

Looks interesting, being a Whoop user for the last few years, I have seen for myself that their AI Coach/AI based suggestions are a hit or miss 3 out of 10 times, slightly concerned about how accurate this will. Not a diabetic patient, but I do monitor my levels with a CGM from time to time, will definitely check it out!

The issue with Whoop’s AI is that there isn’t much data, and the data doesn’t have much prescriptive power, so it can’t really suggest anything useful. Recovery and Strain scores are made up, and even resting heart rate doesn’t tell you anything prescriptive for the day.

The data available to the LLM in OP’s app is the polar opposite. It’s all actíonable and real, so I bet it can draw more useful insights than Whoop reminding you that you didn’t exercise all week.

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

#26

Post by jevogel »

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…

Look into Eversense 355 (implantable), it has so much better accuracy for my wife than the Libre and Dexcom CGMs she has tried.

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

#27

Post by andersonpico »

AnthonBerg wrote: post #17
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.

My experience is completely the opposite, of using LLMs to pattern match and cast diagnostic nets. Is your perspective based on, say, opinionated principle?, or experience? The benefits are enormous. The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.

> No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.

if you can't trust this thing then what is it doing? the implication that people that trust this software do not have adult competency is also confusing.

> Is your perspective based on, say, opinionated principle?, or experience?

your perspective is solely based on recent trauma so I don't know if it is more reliable in any capacity

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

#28

Post by consp »

AnthonBerg wrote: post #17
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.

My experience is completely the opposite, of using LLMs to pattern match and cast diagnostic nets. Is your perspective based on, say, opinionated principle?, or experience? The benefits are enormous. The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.

> The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.

My local physician says otherwise, with respect to facebook posts about dosages. I'm convinced the same applies to LLM generated content with respect to people blindly following the computer.

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

#29

Post by mexicocitinluez »

AnthonBerg wrote: post #17
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.

My experience is completely the opposite, of using LLMs to pattern match and cast diagnostic nets. Is your perspective based on, say, opinionated principle?, or experience? The benefits are enormous. The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.

> 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.

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

#30

Post by mexicocitinluez »

So, I'm in the medical field building an EMR and LLMs have obviously been a really important topic in the industry the last few years. We're still not even sure that giving LLM-assisted suggestions TO ACTUAL DOCTORS AND CLINICIANS will be helpful let alone to the patient themselves.

It's breaking the golden rule of these tools which is to have someone with enough knowledge to verify the accuracy of the data it spits out. Patient's famously don't. Hell, even the actual staff don't really understand or know how these tools work (or the ways in which you can/can't trust them).

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