Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To address this challenge, in this study, we establish a standardized dataset and evaluation framework for medical imaging QC, systematically assessing large language models (LLMs) in image quality assessment and report standardization. Specifically, we first constructed and anonymized a dataset of 161 chest X-ray (CXR) radiographs and 219 CT reports for evaluation. Then, multiple LLMs, including Gemini 2.0-Flash, GPT-4o, and DeepSeek-R1, were evaluated based on recall, precision, and F1 score to detect technical errors and inconsistencies. Experimental results show that Gemini 2.0-Flash achieved a Macro F1 score of 90 in CXR tasks, demonstrating strong generalization but limited fine-grained performance. DeepSeek-R1 excelled in CT report auditing with a 62.23\% recall rate, outperforming other models. However, its distilled variants performed poorly, while InternLM2.5-7B-chat exhibited the highest additional discovery rate, indicating broader but less precise error detection. These findings highlight the potential of LLMs in medical imaging QC, with DeepSeek-R1 and Gemini 2.0-Flash demonstrating superior performance.

本文针对传统医疗影像质量控制方法的劳动密集型和主观性问题，建立了一个标准化的数据集和评估框架。研究中评估了多种大型语言模型在影像质量评估中的表现，发现Gemini 2.0-Flash在胸部X光任务中表现优异，而DeepSeek-R1在CT报告审核中展现了最高的召回率。这些发现表明，大型语言模型在医疗影像质量控制中的潜力，尤其是DeepSeek-R1和Gemini 2.0-Flash的优越性能。

Multimodal Human-AI Synergy for Medical Imaging Quality Control: A
  Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop
  Evaluation

医疗影像质量控制的多模态人机协同：一种具有自适应数据集整理和闭环评估的混合智能框架