CLARITY：用于路由、推理和分诊的临床助手
CLARITY: Clinical Assistant for Routing, Inference, and Triage
AI总结：
提出AI平台CLARITY，结合有限状态机与LLM协作智能体实现患者路由、咨询与病情评估，集成至全国院间平台，验证显示其路由精度超越人类且咨询时间缩短3倍。
摘要：
我们提出了CLARITY（用于路由、推理和分诊的临床助手），这是一个AI驱动的平台，旨在促进患者到专科医生的路由、临床咨询以及患者病情的严重程度评估。其混合架构结合了用于结构化对话流的有限状态机（FSM）与采用大型语言模型（LLM）来分析症状并优先将患者转诊给合适专科医生的协作智能体。CLARITY建立在模块化微服务框架之上，确保了安全、高效和稳健的性能，且灵活并易于扩展，以满足医疗保健领域现有工作流程和IT解决方案的需求。我们报告了将我们的临床助手集成到大规模全国院间平台的情况，在部署的两个月内完成了超过55,000次内容丰富的用户对话，其中2,500次由专家进行了注释以供后续验证。验证结果表明，CLARITY在首次尝试路由精度方面超越了人类水平，顺理成章地使得咨询所需的时间比人工缩短了多达3倍。
英文摘要：
We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patient conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare. We report integration of our clinical assistant into a large-scale national interhospital platform, with more than 55,000 content-rich user dialogues completed within the two months of deployment, 2,500 of which were expert-annotated for subsequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human.
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。
