用多智能体协作提升放射科报告印象生成质量
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System
- 分三步：检索相似报告、生成印象、审查反馈
- 相比单模型，诊断准确率与表达清晰度显著提升
- 适合医疗AI研发者和放射科辅助系统设计者
本研究提出名为RadCouncil的多智能体大语言模型框架，用于提升放射科报告中印象部分的生成质量。该框架包含三个专用智能体：1）检索智能体从向量数据库中查找相似报告；2）放射科医生智能体基于原始发现内容及检索到的范例生成印象；3）评审智能体对生成结果评估并提供反馈。以胸部X光为案例，通过BLEU、ROUGE、BERTScore等量化指标以及GPT-4的定性评估验证性能。实验显示，相较于单智能体方法，RadCouncil在诊断准确性、风格一致性与表达清晰度方面均有显著提升。研究证明，多个分工明确的LLM智能体协同可有效增强专业医疗任务的表现，推动更稳健、可扩展的医疗AI系统发展。
原文摘要 · Abstract (English)
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.
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