Correct Before It Drifts: Counterfactual-Guided Intervention to Mitigate Hallucinations in Radiology Report Generation

Factual errors in radiology reports are usually evaluated after generation, yet they arise sequentially: once an autoregressive generator commits to an image-inconsistent statement, that partial report becomes context for all subsequent sentences. We study this intermediate failure mode as partial-report drift and ask a more targeted question: when an image-grounded correction is available, should it be applied now, before the remaining report is generated? We introduce Correct Before It Drifts, a three-stage intervention framework that does not update the report-generator parameters. First, Grounded Error Detection verifies newly generated clinical claims against the radiograph and proposes only minimal image-supported repairs. Second, Counterfactual Preference Learning constructs corrected and unchanged continuations from the same partial report and uses their complete-report outcomes as offline preference supervision for a lightweight shared model. Third, Online Repair Selection predicts this preference from only a short lookahead and selectively applies repairs using a validation-calibrated intervention threshold. Across MIMIC-CXR and IU-Xray, multiple report generators, and strong hallucination-mitigation baselines, the method consistently reduces report-level clinical errors and improves structured factuality while largely preserving report semantics. Process-level analyses further show that short lookahead predicts full-report preference better than local verification alone and that earlier repairs exert a larger effect on subsequent report content. These results support treating radiology hallucination mitigation as a timely decision over evolving reports rather than only a post-hoc correction problem.