MedGuards: 用于可靠医疗错误检测与纠正的多智能体系统
MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
AI总结：
提出MedGuards框架，通过多智能体上下文学习与置信度引导仲裁机制，无需额外训练即可检测、定位和纠正医疗文本错误，并引入关键词优先纠正评分(KPCS)评估指标。
摘要：
随着大型语言模型（LLMs）在医疗场景中的部署日益增多，对生成或现有文本中的错误进行准确检测和纠正变得至关重要，因为即使是微小的错误也可能对患者安全构成风险。现有的错误检测和纠正方法，包括自动检查和基于启发式的方法，在未见过的数据集上泛化能力不佳。在本文中，我们提出MedGuards作为医疗安全护栏，这是一个将医疗错误检测和纠正视为多智能体上下文学习任务的新框架。专门的智能体分别检测、定位和纠正错误，而置信度引导的仲裁机制则利用推理轨迹和置信度分数来解决分歧。这种设计增强了可解释性、鲁棒性和适应性，无需对基础LLM进行额外训练。此外，我们引入了关键词优先纠正评分（KPCS），这是一种新的评估指标，考虑参考文本中的关键关键词是否被正确生成，从而提供比传统指标更全面的评估。在包含临床笔记的四个多语言医疗数据集上的实验表明，所提出的框架在多个指标和模型上均取得了显著改进。我们的目标是使LLM在现实医疗应用中的部署更加安全。为便于复现，我们在以下网址公开了代码：https://this URL。
英文摘要：
As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even minor mistakes can pose risks to patient safety. Existing methods for error detection and correction, including automated checks and heuristic-based approaches, do not generalize well across unseen datasets. In this paper, we propose MedGuards as a medical safety guardrail, which is a new framework that treats medical error detection and correction as a multi-agent in-context learning task. Specialized agents separately detect, localize, and correct errors, while a confidence-guided arbitration mechanism resolves disagreements using reasoning traces and confidence scores. This design enhances interpretability, robustness, and adaptability, without requiring additional training of the base LLMs. Additionally, we introduce the Keyword-Prioritized Correction Score (KPCS), a new evaluation metric that considers whether critical keywords within the reference text are generated correctly, providing a more comprehensive assessment than conventional metrics. Experiments across four multilingual medical datasets consisting of clinical notes demonstrate significant improvements by the proposed framework across several metrics and models. Our aim is to enable safer deployment of LLMs in real-world healthcare applications. For reproducibility, we make our code publicly available at https://github.com/congboma/MedGuards.
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。
