A world of pure imagination: AI, diabetes and the data we can’t get at
Search the app stores for diabetes and AI and you will find a remarkable number of applications doing exactly one thing. You point a camera […]
Data Analysis
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…