Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast imaging, aiding in the early detection and characterization of breast abnormalities. Interpreting breast ultrasound images commonly involves creating comprehensive medical reports, containing vital information to promptly assess the patient's condition. However, the ultrasound imaging system necessitates capturing multiple images of various parts to compile a single report, presenting a time-consuming challenge. To address this problem, we propose the integration of multiple image analysis tools through a LangChain using Large Language Models (LLM), into the breast reporting process. Through a combination of designated tools and text generation through LangChain, our method can accurately extract relevant features from ultrasound images, interpret them in a clinical context, and produce comprehensive and standardized reports. This approach not only reduces the burden on radiologists and healthcare professionals but also enhances the consistency and quality of reports. The extensive experiments shows that each tools involved in the proposed method can offer qualitatively and quantitatively significant results. Furthermore, clinical evaluation on the generated reports demonstrates that the proposed method can make report in clinically meaningful way.

通过LangChain和大型语言模型(Large Language Models, LLM)的组合，我们提出了一种整合多种图像分析工具的方法，可以准确提取超声图像的相关特征，将其在临床背景中进行解释，并生成全面且标准化的报告，从而减轻放射科医师和医疗保健专业人员的负担，提高报告的一致性和质量。大量实验证明了所提出方法中涉及的每个工具均能提供定性和定量上显著的结果。此外，对所生成报告的临床评估表明，所提出的方法可以以临床意义的方式进行报告生成。

利用LangChain生成乳腺超声报告