Enhancing Image-Text Alignment in Chest X-ray Datasets by Reducing External References via a Fine-Tuned Large Language Model Pipeline.

Large-scale chest X-ray report datasets are widely used to train multimodal models for medical report generation. However, these datasets often contain longitudinal or comparative expressions that implicitly refer to prior examinations. When the corresponding prior images are unavailable, such references create dataset-level inconsistencies and may propagate non-self-contained language patterns to downstream models. To improve report self-containment, this study develops an automated, data quality-oriented preprocessing pipeline that rewrites radiology reports at the sentence level to reduce external references. The pipeline uses an open-source large language model fine-tuned with a parameter-efficient method on a small set of reports manually revised according to predefined editing guidelines that preserve grammatical coherence. The pipeline is applied to the MIMIC-CXR dataset. Content containing external references is quantified using keyword-based statistics and a discriminative detection model. The edited dataset is then used to fine-tune a vision-language model for report generation. External references are substantially less prevalent in the edited reports than in reports from the original MIMIC-CXR dataset and a previously published cleaned dataset. The vision-language model fine-tuned on the edited dataset produces fewer statements that rely on unavailable longitudinal context. Automated report editing can therefore serve as an effective preprocessing step for improving the suitability of chest X-ray report datasets for multimodal model development. The proposed pipeline is open source, computationally efficient, and suitable for fully local deployment in privacy-sensitive medical data environments.