Detecting Clinical Trial Risks Before Protocol Lock

Clinical trial delays often originate in protocol decisions that appear reasonable in isolation but conflict once eligibility, sample size, endpoint burden, site capacity, and enrollment assumptions interact. The interview explains how AI can structure protocol attributes, compare them with internal and external evidence, and surface reviewable risk flags before protocol lock. It also distinguishes practical MVPs from use cases that depend on standardized historical trial data, and argues for human oversight, validation, governance, integration, and MLOps. Leaders should treat this as a governed decision-support capability—not autonomous protocol design—and prioritize the recurring risks that create the most rework in their own portfolios.