通过多智能体系统增强LLM在放射学报告印象生成中的能力
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System
AI总结：
提出多智能体LLM框架RadCouncil，通过检索、放射科医生和审阅者协作生成放射报告印象，并在胸部X光案例中验证其优于单智能体方法。
摘要：
本研究介绍了“RadCouncil”，这是一个多智能体大型语言模型（LLM）框架，旨在根据发现部分增强放射学报告中的印象生成。RadCouncil包含三个专门智能体：1）“Retrieval”智能体，从向量数据库中识别并检索相似报告；2）“Radiologist”智能体，根据给定报告的发现部分以及Retrieval智能体检索到的范例报告生成印象；3）“Reviewer”智能体，评估生成的印象并提供反馈。研究以胸部X光为案例，使用定量指标（BLEU、ROUGE、BERTScore）和由GPT-4评估的定性标准对RadCouncil的性能进行了评价。实验结果表明，RadCouncil在诊断准确性、风格一致性和清晰度等多个维度上均优于单智能体方法。本研究强调了利用多个相互交互、各自承担专门任务的LLM智能体来提升专业医疗任务性能，并开发更稳健、更具适应性的医疗AI解决方案的潜力。
英文摘要：
This study introduces "RadCouncil," a multi-agent Large Language Model (LLM) framework designed to enhance the generation of impressions in radiology reports from the finding section. RadCouncil comprises three specialized agents: 1) a "Retrieval" Agent that identifies and retrieves similar reports from a vector database, 2) a "Radiologist" Agent that generates impressions based on the finding section of the given report plus the exemplar reports retrieved by the Retrieval Agent, and 3) a "Reviewer" Agent that evaluates the generated impressions and provides feedback. The performance of RadCouncil was evaluated using both quantitative metrics (BLEU, ROUGE, BERTScore) and qualitative criteria assessed by GPT-4, using chest X-ray as a case study. Experiment results show improvements in RadCouncil over the single-agent approach across multiple dimensions, including diagnostic accuracy, stylistic concordance, and clarity. This study highlights the potential of utilizing multiple interacting LLM agents, each with a dedicated task, to enhance performance in specialized medical tasks and the development of more robust and adaptable healthcare AI solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
