Breast Ultrasound Report Generation using LangChain
This work addresses the problem of efficient and consistent breast ultrasound report generation for radiologists and healthcare professionals, though it appears incremental as it combines existing tools and models.
The paper tackles the time-consuming challenge of generating comprehensive medical reports from multiple breast ultrasound images by integrating multiple image analysis tools with Large Language Models via LangChain, resulting in a method that accurately extracts features, interprets them clinically, and produces standardized reports, reducing burden on radiologists and enhancing consistency.
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.