Diagnostic and interventional radiology (Ankara, Turkey). 2025 May 20. doi: 10.4274/dir.2025.253272 Q31.72025

Retrieval-augmented generation for answering Breast Imaging Reporting and Data System (BI-RADS)-related questions with large language models

基于大型语言模型的乳腺影像报告和数据系统（BI-RADS）相关问题检索增强生成回答的方法翻译改进

Esat Kaba¹

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¹ Recep Tayyip Erdoğan University Training and Research Hospital, Department of Radiology, Rize, Türkiye.

DOI: 10.4274/dir.2025.253272 PMID: 40391698

摘要中英对照阅读

Keywords：retrieval augmented generation; breast imaging reporting; data system related questions; large language models

关键词：检索增强生成; 乳腺影像报告; 数据系统相关问题; 大型语言模型

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期刊名：Diagnostic and interventional radiology

缩写：DIAGN INTERV RADIOL

ISSN：1305-3612

e-ISSN：1305-3612

IF/分区：1.7/Q3

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Retrieval-augmented generation for answering Breast Imaging Reporting and Data System (BI-RADS)-related questions with large language models

基于大型语言模型的乳腺影像报告和数据系统（BI-RADS）相关问题检索增强生成回答的方法翻译改进

Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework

大型语言模型在肝病临床指南解释中的优化：基于检索增强生成框架的方法

Retrieval-augmented generation salvages poor performance from large language models in answering microbiology-specific multiple-choice questions

检索增强生成可改善大型语言模型在回答微生物学特定选择题方面的较差表现

Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications

基于大型语言模型的检索增强生成在肾病学中的应用集成：推动实际应用进展

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study

基于检索增强生成技术改进大型语言模型在糖尿病教育中的性能：比较研究

[Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented Generation]

与大型语言模型的优化交互：提示工程和检索增强生成的实际指南

Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models

检索增强生成与基于文档的生成：大型语言模型中的关键区别

Authors' reply: Re: Koga et al. Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models

作者回复：关于小川等人的评论：“检索增强生成与文档基础生成：大型语言模型中的关键区别”

Improving Automated Deep Phenotyping Through Large Language Models Using Retrieval Augmented Generation

基于检索增强生成的大语料模型在自动化深度表型中的应用研究

Enhancing Large Language Models with Retrieval-augmented Generation: A Radiology-specific Approach

利用检索增强生成改进大型语言模型：一种特定于放射学的方法

Retrieval-augmented generation for answering Breast Imaging Reporting and Data System (BI-RADS)-related questions with large language models

基于大型语言模型的乳腺影像报告和数据系统（BI-RADS）相关问题检索增强生成回答的方法 翻译改进

Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework

大型语言模型在肝病临床指南解释中的优化：基于检索增强生成框架的方法

Retrieval-augmented generation salvages poor performance from large language models in answering microbiology-specific multiple-choice questions

检索增强生成可改善大型语言模型在回答微生物学特定选择题方面的较差表现

Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications

基于大型语言模型的检索增强生成在肾病学中的应用集成：推动实际应用进展

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study

基于检索增强生成技术改进大型语言模型在糖尿病教育中的性能：比较研究

[Optimized interaction with Large Language Models : A practical guide to Prompt Engineering and Retrieval-Augmented Generation]

与大型语言模型的优化交互：提示工程和检索增强生成的实际指南

Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models

检索增强生成与基于文档的生成：大型语言模型中的关键区别

Authors' reply: Re: Koga et al. Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models

作者回复：关于小川等人的评论：“检索增强生成与文档基础生成：大型语言模型中的关键区别”

Improving Automated Deep Phenotyping Through Large Language Models Using Retrieval Augmented Generation

基于检索增强生成的大语料模型在自动化深度表型中的应用研究

Enhancing Large Language Models with Retrieval-augmented Generation: A Radiology-specific Approach

利用检索增强生成改进大型语言模型：一种特定于放射学的方法

基于大型语言模型的乳腺影像报告和数据系统（BI-RADS）相关问题检索增强生成回答的方法翻译改进