Most clinical AI calls a cloud API, so the record leaves the building. A model on a 2 GB-RAM CPU keeps inference on-device. We built Duan that way: github.com/wipen/Duan. For sensitive care, should the default be cloud convenience or edge privacy? #HealthAI #DigitalHealth

The secure-tool argument is right, but secure usually means cloud with a BAA. Local, on-device models flip the risk model: no patient data leaves the machine, so the leak surface shrinks toward zero. Worth separating secure transport from data-never-leaves. #HealthAI

Why open source for clinical AI? So you can audit the logic, run it offline, and adapt it to your own population without sending a byte of patient data anywhere. Duan is on GitHub: github.com/wipen/Duan #opensource #HealthAI #clinicalAI #reproducible

Design choice: Duan keeps the decision logic explicit and inspectable, not a black box, so a clinician can see why it flagged something. At the edge, interpretability matters more than raw accuracy. What would make you actually trust a local model enough to use it? #HealthAI #opensource

What if the AI layer at the bedside needed no cloud and no GPU? Duan runs clinical decision models on CPU with 2GB RAM, fully local. Open source: github.com/wipen/Duan #HealthAI #opensource #MedAI #DigitalHealth

System 1 here means fast, heuristic support that fires before the slow deliberate review: triage signals, risk flags, protocol checks at the point of care. Which workflow would you structure first, triage, dosing, or guideline matching? #HealthAI #opensource #MedAI