Reply: evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System (BI-RADS) Atlas 5th edition

Abstract

We sincerely thank the author for their insightful comments1 and valuable suggestions regarding our manuscript titled “Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5th edition.2 We appreciate the author’s interest and the constructive proposal to incor- porate retrieval-augmented generation (RAG) methodologies.1 We fully agree that employing RAG could enhance the accuracy, contextual relevance, and reliability of responses generated by large language models (LLMs), particularly when ad- dressing complex clinical scenarios such as those encountered in breast radiology.2 As noted, RAG effectively mitigates limitations inherent in static models, including knowledge gaps and the risk of hallucinations, by dynamically retrieving relevant external information.3,4