用大模型提升医学影像质检效率，实测多个模型表现各异。
Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation
- 构建161张胸片+219份报告的标准化数据集，评估大模型质检能力。
- Gemini 2.0-Flash在胸片任务中宏平均F1达90，DeepSeek-R1报告审计召回率62.23%领先。
- 提出闭环评估框架，适合医疗AI开发者与影像科医生参考。
医学影像质量控制对准确诊断至关重要，但传统方法依赖人力且主观性强。本研究建立标准化数据集与评估框架，系统评估大语言模型（LLMs）在图像质量评估与报告标准化中的表现。首先构建并匿名化包含161张胸部X光片（CXR）和219份CT报告的数据集。随后基于召回率、精确率与F1分数，评估Gemini 2.0-Flash、GPT-4o与DeepSeek-R1等多款模型在检测技术错误与不一致方面的性能。实验表明，Gemini 2.0-Flash在胸片任务中达到90的宏平均F1，具备强泛化能力但细粒度表现有限；DeepSeek-R1在CT报告审核中召回率达62.23%，优于其他模型；其精简版本表现不佳，而InternLM2.5-7B-chat则展现出最高额外发现率，说明其覆盖范围广但精度较低。结果凸显大模型在医学影像质量控制中的潜力，尤其以DeepSeek-R1与Gemini 2.0-Flash表现突出。
原文摘要 · Abstract (English)
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.
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