  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.
