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

• URL: http://arxiv.org/abs/2503.07032v1
• Date: Mon, 10 Mar 2025 08:16:18 GMT
• Title: Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation
• Authors: Zhi Qin, Qianhui Gui, Mouxiao Bian, Rui Wang, Hong Ge, Dandan Yao, Ziying Sun, Yuan Zhao, Yu Zhang, Hui Shi, Dongdong Wang, Chenxin Song, Shenghong Ju, Lihao Liu, Junjun He, Jie Xu, Yuan-Cheng Wang,
• Abstract summary: We evaluate large language models (LLMs) in image quality assessment and report standardization.<n> Gemini 2.0-Flash achieved a Macro F1 score of 90 in CXR tasks, demonstrating strong generalization but limited fine-grained performance.<n>DeepSeek-R1 excelled in CT report auditing with a 62.23% recall rate, outperforming other models.
• Score: 16.19033330311087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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