Large language model consensus for reliable research cohort construction from radiology reports: a retrospective cohort study

Background Large-scale retrospective studies often require researchers to adjudicate outcomes or cohort eligibility from unstructured clinical records. Manual review can provide reliable labels but is difficult to scale. We developed and evaluated a consensus workflow using multiple large language models (LLMs) to automatically assign high-confidence outcome labels while deferring ambiguous cases for manual review. Methods We included 6,718 brain MRI reports from 5,856 patients. The expert-adjudicated reference set included 680 reports from 675 patients: 372 normal (age-appropriate without sig

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