ClinOPD: On-Policy Distillation with Clinical Hints for Medical Report Generation

Automatic medical report generation aims to generate clinically accurate reports from medical images. Existing approaches learn the mapping from images to reference reports, but generated reports may still miss clinically important findings or introduce unsupported abnormalities. Inspired by the clinical reporting workflow, we introduce Clinical Hints, a compact signal that retains only the organs associated with positive findings. We find that providing such hints to the same model substantially improves report generation, suggesting that relevant clinical knowledge is often already encoded in the model but is not reliably activated from medical images alone. Based on this observation, we propose ClinOPD, an on-policy self-distillation framework that uses clinical hints as privileged supervision. A frozen hint-conditioned teacher provides token-level guidance along student-generated trajectories, while the student takes only the medical image as input and is regularized toward the original policy for stable optimization. No clinical hints are required at inference time. We evaluate clinical correctness using a factualness-driven F1 metric based on matched, missed, and unsupported findings, which shows stronger agreement with physician judgments than conventional report-generation metrics. Across the 2D IU-Xray and 3D CT-RATE datasets, ClinOPD improves F1 from 46.06 to 51.12 and from 23.65 to 30.06, corresponding to relative gains of 11.0% and 27.1% over SFT, respectively, while outperforming competing reinforcement-learning and on-policy distillation methods. Code will be made available.