# RetinaGuard

Multi-disease retinal screening needs a per-image decision on whether an automated call can stand without review. RetinaGuard combines evidence from quality, diagnostic, uncertainty, verification and explanation agents into a learned probability that a decision is unsafe, invokes tools only when their expected value exceeds their cost, and certifies the acceptance threshold with conformal risk control. On the RFMiD test split with the weaker primary model (macro-AUROC 0.896), it captured 119 of 280 unsafe decisions at a 20% referral budget against 88 for lowest-confidence referral; with the stronger model (0.925) the verifier's gain shrank to a few percentage points.

Multi-disease retinal screening needs a per-image decision on whether an automated call can stand without review. RetinaGuard combines evidence from quality, diagnostic, uncertainty, verification and explanation agents into a learned probability that a decision is unsafe, invokes tools only when their expected value exceeds their cost, and certifies the acceptance threshold with conformal risk control. On the RFMiD test split with the weaker primary model (macro-AUROC 0.896), it captured 119 of 280 unsafe decisions at a 20% referral budget against 88 for lowest-confidence referral; with the stronger model (0.925) the verifier's gain shrank to a few percentage points.