Recently, we became aware that i-Sens has published a marketing infographic promoting the CareSens Air continuous glucose monitoring (CGM) system. The infographic references Diabettech and […]
I’d been using an Accuchek Insight for getting on for five years, starting in 2020 with a hand me down, then obtaining one of the […]
Taking the evaluation of the nine sensors a step further with Consensus Error Grids and looking at the price performance of the sensors.
9 sensors; 15 days; 150 fingerpricks. The results of the “nine sensor samba” are in, and they’re not what we were expecting…
Previewing the “Nine Sensor Samba”: Our real-world comparison of nine CGMs simultaneously, assessing accuracy (MARD), Time-in-Range, lag, and app usability.
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 […]
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.
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 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 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…
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