基于共识，紧跟新兴科学：用于长期新冠的共识锚定多语料临床聊天机器人

中文摘要 AI 辅助

长期新冠（LC）给临床决策支持带来挑战，因为相关证据分布在不同更新周期、证据作用和临床成熟度的来源中。我们提出了一个面向临床医生的聊天机器人，它在检索增强工作流程中整合了四个来源：专家策划的共识指南、当前的PubMed文献、注册的干预试验以及来自实时系统评价的证据。共识指南始终用于构建回复，而其余来源在用户选择时并行检索。在对50个面向临床医生的问题进行的探索性自动评估中，我们的聊天机器人显示出与OpenEvidence相当的平均评分，在由语言模型判断的比较中，得分在数值上更高且得分变异性更低。

英文摘要

Long COVID (LC) poses a challenge for clinical decision support because relevant evidence is distributed across sources with different update cycles, evidentiary roles, and levels of clinical maturity. We present a clinician-facing chatbot that organizes four sources within a retrieval-augmented workflow: expert-curated consensus guidance, current PubMed literature, registered interventional trials, and evidence from living systematic reviews. Consensus guidance is always included to frame responses, while the remaining sources are retrieved in parallel when selected by the user. In an exploratory automated evaluation on 50 clinician-facing questions, our chatbot showed comparable mean ratings to OpenEvidence, with numerically higher scores and lower score variability in an LLM-judged comparison.

发表机构

• University of Texas at Austin（德克萨斯大学奥斯汀分校）

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