How to move from a public-health or clinical question to a study design capable of answering it credibly: design selection, bias, confounding, and the structural decisions that determine whether a study’s conclusions will hold up.
When AI determines whether an exposure, outcome, phenotype or case is present, model error becomes epidemiological measurement error. Accuracy alone is not enough; validation must follow the variable's role in inference.
AI predictions can trigger public-health actions that alter the population and the data later used to evaluate the same model. Prediction, intervention and measurement then become causally coupled.
Regression adjustment can estimate a conditional association under a specified model. It cannot decide the causal question, fix selection created by design, or determine whether a variable is a confounder, mediator or collider.
AI can accelerate retrieval, drafting, coding and analytical support. Decisions that define the epidemiological object, causal question, exclusions, interpretation and publication accountability require explicit human ownership.
A finished-looking protocol and a protocol whose design reasoning survives review are different documents. Journey is built to produce the second one.
A public-health concern becomes researchable only after its population, exposure or intervention, comparator, outcome, and time frame are explicit — and after the question is tested for feasibility, relevance, ethics, and decision value.