Inference

What happens when a trained model is actually used, taking an input and producing an output. It is the counterpart to training: training is how the model is built, inference is every time it is run afterward. The word comes up around cost and speed, because each use consumes computing power, and a tool that is cheap to use once can be expensive across a whole health system. For clinical use the practical point is that the model is not learning from your case during inference unless it has been specifically set up to, so it is applying what it already learned, not adapting to you in the moment.

Terms like this come up in real clinical scenarios across the HelloAI courses: bite-sized modules with verifiable certificates. An account takes one minute, no password needed.