In health, saying “the model is accurate” is not enough to guide a decision. We need to know what task it was evaluated for, where the data came from, what an error could mean, and how performance is assessed in the real setting.
A research framework for AI in the medication-use process brings evaluation, human oversight, explainability, and governance together. Its value is that it does not reduce safety to one score or accuracy chart.
Before adding a feature, a simple note can define permitted and prohibited uses, trusted inputs, important failure modes, the point for human review, and a way to switch the feature off. This does not guarantee safety, but it makes assumptions visible and review more practical.