Wholistic report generation for Breast ultrasound using LangChain
Abstract
Breast ultrasound (BUS) is a vital imaging technique for detecting and characterizing breast abnormalities. Generating comprehensive BUS reports typically requires integrating multiple image views and patient information, which can be time-consuming for clinicians. This study explores the feasibility of a modular, AI-assisted framework to support BUS report generation, focusing on system integration. We developed a suite of classification networks for image analysis, coordinated via LangChain with Large Language Models (LLMs), to generate structured and clinically meaningful reports. A Retrieval-Augmented Generation (RAG) component allows the framework to incorporate prior patient information, enabling context-aware and personalized report generation. The system demonstrates the practical integration of existing image-analysis models and language-generation tools within a clinical workflow. Experimental evaluations show that the integrated framework produces consistent and clinically interpretable reports, which align well with radiologists' assessments. These results suggest that the proposed approach provides a feasible, modular, and extensible solution for semi-automated BUS report generation, offering a foundation for further refinement and potential clinical deployment.
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