Evaluating large language models in echocardiography reporting: opportunities and challenges

Abstract

Aims: The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results: = 0.42) and automatic metrics showed insensitivity (0-5% drop) to changes in measurement numbers. Conclusion: EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

Ähnliche Arbeiten