Knowledge distillation-based lightweight generative large language models for standardized quality control of Chinese radiology reports

Radiology reports, which document radiologists’ imaging observations and diagnostic conclusions from CT, X-ray and MRI scans, deliver core imaging evidence for clinical diagnosis and therapeutic planning. Inaccurate or disordered reports may lead to misdiagnosis and delayed intervention, posing risks to patient safety. Radiology reports are essential for clinical decision-making, yet Chinese radiology reports frequently exhibit logical inconsistencies, incomplete content, non-standardized descriptions, and formatting errors, while manual quality control remains time-consuming and inconsistent. Large teacher models can provide task-specific supervision but are difficult to deploy locally, whereas smaller models require adaptation to this clinical text task. We developed a teacher-supervised, locally deployable 4B language-model system for rule-based textual quality control of Chinese radiology reports. Among the three evaluated teacher APIs, Doubao-seed1.6-non-thinking achieved the highest macro-precision of 0.94, under the study’s precision-prioritized selection. Qwen3-4B-Instruct-2507 emerged as the optimal student model, achieving a macro-averaged precision of 0.90, recall of 0.49, and F1-score of 0.62. Under the deployment conditions of this study, it yielded an observed inference speed improvement of over 40-fold. Real-world clinical validation demonstrated that the lightweight model attains an alert acceptance rate of 0.74 in authentic clinical scenarios, suggesting it can serve as an adjunct for human radiologist review.