Sure thing! Start with a modular design: split the AI into three layers—data ingestion, decision logic, and safety override. For data ingestion, pull in vital signs, patient history, and even voice cues from a wearable. The decision logic can use a rule‑based core plus a lightweight neural net that flags red flags and suggests next steps. Then add a safety override that always checks against a curated safety list; if anything looks risky, it pauses the recommendation and alerts a human. Keep the model transparent so clinicians can see why a suggestion was made—trust is key. Use continuous learning but lock updates behind a rigorous testing pipeline, so you never deploy a model that’s less safe than the last version. And if it gets stuck, just ping a backup rule‑based engine—human backup is the only safe bet in the field.

Nice call on the drift monitor—add a rolling baseline for each sensor, compare the current reading to that, and flag anything beyond a threshold. Keep the logs in JSON so you can grep them fast. That way you catch a glitch before it bubbles up to the decision layer. Stay tight on that safety gate, and we’ll keep patient care on point.

Sounds good—let’s lock it in and start testing the drift alarms. If anything pops up, we’ll flag it right away. Keep me posted on the rollout.