ExpertRoute: Distilling Radiologist Visual Search into Compact Evidence for Chest X-Ray Report Generation

For chest X-ray report generation, visual context is useful only if it preserves the evidence needed for faithful diagnostic description. We introduce ExpertRoute, which distills radiologists’ visual-search behavior into an image-conditioned expert prior and combines it with independent representation-space semantic coverage under an exact visual-context budget. The expert prior is learned once from eye tracking and requires no gaze at inference. On a frozen LLaVA-Rad backbone, ExpertRoute reduces the visual context entering the LLM from 1,369 to 192 tokens (14.0%). Despite this reduction, it improves RadGraph and GREEN over full-context inference across three evaluation cohorts: MIMIC Validation, a locked MIMIC held-out cohort, and OpenI. On the locked held-out cohort, these RadGraph and GREEN gains are supported by patient-cluster bootstrap. Across the evaluated token budgets, ExpertRoute exhibits a non-monotonic response, with Micro-F1, Macro-F1, and RadGraph all reaching their best observed values at 192 tokens. At this operating point, the compact visual context also reduces per-sample generation latency by 16.2% relative to full-context inference. These results show that report quality depends on how visual evidence is composed, not simply on how much context is retained.