A recent deep dive into Cochlear’s Nucleus Nexa System shows what edge AI looks like when it has to live inside the human body for decades. The system uses an environmental classifier called SCAN 2 to categorise sound as speech, speech-in-noise, music, noise, or quiet. A decision tree model then maps these classifications to sound processing strategies, adjusting the electrical signals sent to the implant in real time.

What makes this example stand out is the combination of machine learning with extreme power constraints. The implant and external sound processor share intelligence via a low-power RF link, using Dynamic Power Management to keep the device operational over a 40+ year lifespan. On top of that, spatial processing features like ForwardFocus automatically reduce background noise using dual microphones, without adding extra decision-making burden on the wearer.

The other breakthrough is upgradeability. Historically, once an implant was in place, its internal technology was effectively frozen. With the Nucleus Nexa implant, firmware updates can now be pushed into the device itself, not just into the external hardware, while still relying on close-range, carefully controlled update channels. Cochlear is using this to store a user’s personalised hearing map directly on the implant and is exploring future upgrades toward deep neural network models and remote monitoring. It’s a preview of where regulated edge AI is headed: tiny, power-aware models, robust safety constraints, and long-term software life cycles.