Artificial intelligence assisted ultrasound report generation
Year: 2025
Type: article
Abstract: Artificial intelligence (AI) assisted ultrasound report generation represents a technology that leverages artificial intelligence to convert ultrasound imaging analysis results into structured diagnostic reports. By integrating image recognition and natural language generation models, AI systems can automatically detect and analyze lesions or abnormalities in ultrasound images, generating textual descriptions of diagnostic conclusions (e.g. , fatty liver, liver fibrosis, automated BI-RADS grading of breast lesions), imaging findings, and clinical recommendations to form comprehensive reports. This technology enhances the efficiency and accuracy of imaging diagnosis, reduces physicians’ workloads, ensures report standardization and consistency, and provides robust support for clinical decision-making. Current state-of-the-art algorithms for automated ultrasound report generation primarily rely on vision-language models, which harness the generalization capabilities of large language models and large vision models through multimodal (language + vision) feature alignment. However, existing approaches inadequately address challenges such as numerical measurement generation, effective utilization of report templates, incorporation of historical reports, learning text-image correlations, and overfitting under limited data conditions. This paper aims to introduce the current state of research on ultrasound report generation, the existing issues, and to provide some thoughts for future research.
Authors: Jia-Hui Zeng, Kai-Kai Zhao, Ning-Bo Zhao
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411048209