Trusted edge health AI
Health AI
Health AI at the edge needs more than a compact model. It needs dependable sensing, privacy-aware system design, and predictable performance in clinical environments.
The value of keeping intelligence close to the patient
Running inference closer to where health data is generated can reduce latency and improve privacy, but it also shifts more responsibility onto the hardware and software stack deployed in hospitals, labs, and devices.
The imec story here is about trustworthiness through integration: combining sensors, secure processing, and efficient AI in ways that are robust enough for real medical workflows.