Embedded AI is starting to appear in open source AID systems, offered as copy and paste, bring your own key, or a service the project runs. A year of measuring these models says the clinical risk is much the same in all three. The regulatory position is not, and the intuition about which is safest runs backwards.
Ask once and you get an answer. Ask fifty times and you get eighteen different pairs of safety limits, a headline that flips one time in five, and an app that accepts them all. Inside the £4.49 app that turns your diabetes data into chatbot advice on your OS-AID settings.
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 […]
A slightly rhetorical question, I’ll admit, but a pertinent one. Once again, I have been trying out the latest (read most heavily pushed on Facebook […]
We took Nightscout data from three people and gave it to 5 Large Language Models with a detailed prompt to see how they would respond.
The results weren’t what I was expecting.
After 10+ years of documenting the diabetes landscape, Diabettech is launching diabettech.ai—a custom research tool designed to help you navigate a decade’s worth of articles and reports. Powered by an LLM interface, this tool allows you to “interrogate” the site’s data to find specific answers on everything from cures to n=1 observations.
Key highlights of the beta launch:
Targeted Accuracy: Built to stick strictly to Diabettech articles and comments to minimize “hallucinations.”
Research-Focused: Optimized as a precision tool rather than a standard chatbot to provide relevant article links.
Sustainable Design: Privately funded and optimized for efficiency to manage resource costs and query speeds.
Ready to dive into the archives? Visit diabettech.ai to start your research and share your feedback on the beta experience.
Artificial intelligence has been hovering around diabetes care for a while now, usually wrapped in glossy demos and ambitious claims. Carb-counting apps that “just work”. […]
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:
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