Validating an AI-Health Model for Clinical Deployment, Step by Step

Most AI-health models that fail in deployment did not fail statistically. They failed because the population, the workflow, or the label definition shifted between development and use.\n\nThe validation sequence we run before any model reaches a clinician:\n\n1. Retrospective performance on held-out data from the deploying site, not just the development site.\n2. Subgroup analysis across age, sex, ethnicity, and device or assay vendor — reported publicly, not internally.\n3. Silent prospective running, where the model scores live cases and no one sees the output.\n4. Shadow review, where clinicians see the output but do not act on it, and disagreements are adjudicated.\n5. Staged live use with a monitored override rate.\n\nStep three catches the majority of real problems, and it is the step most often skipped because it produces no visible progress. It is also the cheapest place to discover that your label was defined differently in the new site's record system.