Fluent is not correct: reviewing AI in clinical settings

Note: this is an educational piece about evaluation, not medical advice.

A drafted treatment plan reads clearly and confidently. Nothing in it is false. But it never mentions that the patient's kidney function changes how a drug should be dosed.

Omissions hide in plain sight

Reviewers naturally check what is on the page. Mistakes of omission are not on the page, which makes them the easiest to approve and the most serious to miss.

Review against the guideline, not the text

A clinical reviewer compares the answer with what the relevant guidance requires for this specific patient: contraindications, dose adjustments, monitoring, and the order of steps. The question is 'what should be here that is not?'

Turning that into signal

Each missed item becomes a criterion the next model is graded on. Over time, the review process turns individual clinical judgments into a checklist that automated systems can be measured against.