Struggling to turn model results into claims that survive FDA/EMA review? In this lesson, you’ll learn to phrase ML performance using confidence intervals, p-values, and explicit hypothesis frameworks that are specific, bounded, and decision-linked—ready for SaMD dossiers across US/EU. You’ll find concise explanations, regulator‑calibrated examples, and targeted exercises that reinforce compliant language, caveats, and thresholds. Finish with a reusable template that standardizes your team’s voice, reduces queries, and accelerates review cycles.
Regulator‑Ready Language: How to Write Performance Claims for ML Models in SaMD and Enterprise AIStruggling to turn ML results into claims that survive FDA/EMA scrutiny? In this lesson, you’ll learn to write regulator-ready performance statements for SaMD and enterprise AI—anchored to intended use, supported by calibrated statistics and CIs, and bounded by fairness and generalizability limits. You’ll get step-by-step guidance, phrasing templates, worked examples, and quick exercises to test your understanding, so your next submission is precise, reproducible, and defensible.