Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning
Researchers propose a reinforcement learning framework that lets radiology report generators trade off clinical precision against recall at inference time, solving a critical gap in automated medical documentation. Most NLG systems optimize for fluency metrics that ignore clinical alignment, risking reports that read well but miss diagnoses or flag false positives. This work introduces explicit control parameters that let clinicians tune model behavior to match institutional risk tolerance, bridging the gap between language quality and medical safety. The approach signals growing maturity in domain-specific LLM tuning for high-stakes applications.62
