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Abstract

Clinical deployment of foundation models requires decision policies that operate under explicit error budgets, such as a cap on false-positive clinical calls. Strong average accuracy alone does not guarantee safety: errors can concentrate among patients selected for action, leading to harm and inefficient use of healthcare resources. Here we introduce SO_SCPLOWTRATC_SCPLOWCP, a stratified conformal framework that turns foundation model predictions into decision-ready outputs through error-controlled selection and calibrated deferral. SO_SCPLOWTRATC_SCPLOWCP first selects a subset of patients for immediate clinical action while controlling the false discovery rate at a user-specified level. For the remaining patients, it returns prediction sets that achieve target coverage conditional on deferral, supporting confirmatory testing or expert review. When clinical guidelines define relationships among disease states, SO_SCPLOWTRATC_SCPLOWCP incorporates a utility graph to produce clinically coherent prediction sets without sacrificing coverage guarantees. We evaluate SO_SCPLOWTRATC_SCPLOWCP in ophthalmology and neuro-oncology across diagnosis, biomarker prediction, and time-to-event prognosis. Across tasks, SO_SCPLOWTRATC_SCPLOWCP controls the false discovery rate among selected patients and provides valid, selection-conditional coverage for deferred patients. In neuro-oncology, it enables H&E-based diagnosis under a fixed error budget, reducing reliance on reflex molecular assays and lowering laboratory cost and turnaround time. SO_SCPLOWTRATC_SCPLOWCP establishes error-controlled decision policies for safe deployment of medical foundation models.

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