The Nine Sensor Samba: How do the Consensus Error Grids stack up?
Taking the evaluation of the nine sensors a step further with Consensus Error Grids and looking at the price performance of the sensors.
Blog Posts
The nine sensor samba: Results revealed
9 sensors; 15 days; 150 fingerpricks. The results of the “nine sensor samba” are in, and they’re not what we were expecting…
The Nine Sensor Samba — A Sneak Peek at Simultaneous CGM Testing
Previewing the “Nine Sensor Samba”: Our real-world comparison of nine CGMs simultaneously, assessing accuracy (MARD), Time-in-Range, lag, and app usability.
Beyond Snaq-qing. Digesting your glucose patterns.
I spent a long time discussing Snaq’s use of AI for food recognition in the previous article. In this one, I’m looking at what it […]
Snaq. A diabetes AI to help you carb count. Badly.
Here we review Snaq, a diabetes management app that aims to simplify carb counting through food photo recognition. Despite its functionality, the app struggles with food identification and portion sizes, often leading to inaccurate outputs. Users must contribute data to improve its learning, raising concerns about its subscription cost versus effectiveness.
In conversation with… Unpacking the “Why” Behind the Errors
Here we discuss the LLMs design and purpose in the context of interacting with and advising on health data, and demonstrate why the two may be at odds with one another:
https://wp.me/p7O2EL-2BM
In conversation with… File uploads and interpretation. Where the wheels fall off!
In this final section of the “In conversation with” experiment, we provided “access” to complete Nightscout data for the LLM models.
Unfortunately, this is also where the wheels rather fell off, and whilst the results gave an initial appearance of credibility, upon further investigation it became clear that what was presented was rather less then effective and could have been very unsafe.
Read on to learn more: https://wp.me/p7O2EL-2BE
In Conversation with…. Reviewing user data and system settings using AI
In this article, we look at the abilities of the LLMs to review data charts in image form from Nightscout and see what they suggest in terms of changing treatments, and how the contextualise those changes.
As a key way that users are interacting with these models, this is a critical insight into what you might get.
It’s a big read with lots to go through, but we’d highly recommend taking the time. It revealed a lot of unexpected items that are potential gotchas…
In Conversation with…. Carb Counting with AI: A Deep Dive into How Different Models Stack Up
In the dynamic world of diabetes management, anything that can help us accurately estimate carbohydrates in our meals is a game-changer. Imagine a future where […]
In conversation with… Factual questions for Large Language Models
As I stated in the last “In conversation with” article, I split the questions into three sets. Factual, picture analysis and data analysis. This is […]