Evidence-Graded Decision Authorization for Safe Clinical AI: A Constrained Reasoning Framework

Clinical AI systems have achieved strong predictive performance; however, prediction accuracy is not sufficient for clinical safety. Retrieval-augmented generation (RAG) improves factual accuracy, and general-purpose LLM guardrails constrain surface-level output safety, but these mechanisms do not govern the inferential gap between available clinical evidence and permissible clinical claims. We propose Evidence-Graded Decision Authorization (EGDA), a framework that separates evidence extraction, sufficiency assessment, and claim-level authorization through domain-specific rules. In a controlled experiment using 60 breast cancer decision-snapshot cases (1,260 system outputs across three arms evaluated by LLM-as-Judge with expert calibration), EGDA reduced the unjustified inference rate to 8.0% (vs. 48.7% for unconstrained LLM and 47.7% for RAG; risk difference vs. unconstrained -40.7%, 95% CI -46.9 to -34.0, p < 0.001), raised the appropriate refusal rate to 95.0% (vs. 56.9% and 56.9%; risk difference vs. unconstrained +38.1%, 95% CI +31.3 to +44.5, p < 0.001), and achieved the highest factual correctness at 96.4% (vs. 69.8% and 74.5%). An unexpected finding was that retrieval-augmented generation without an authorization gate failed to reduce unjustified inference relative to the unconstrained baseline (47.7% vs. 48.7%, p = 0.870) and produced no improvement in appropriate refusal (56.9% vs. 56.9%, p = 1.0), showing that information supply alone is not sufficient for inferential governance. We argue that domain-specific, evidence-graded reasoning governance should serve as a deployment reference standard for safety-critical clinical AI.