基于共识的多源临床聊天机器人，助力长新冠诊疗决策。
Grounded in Consensus, In Step With Emerging Science: A Consensus-Anchored Multi-Corpus Clinical Chatbot for Long COVID
- 以专家共识为基准，融合文献、试验与动态综述多源信息。
- 在50个临床问题上评分接近OpenEvidence，波动更小。
- 适合临床医生快速获取更新、可靠、结构化证据。
长新冠（LC）的临床决策支持面临挑战，因相关证据分散于不同更新周期、证据等级和临床成熟度的来源中。本文提出一款面向临床医生的聊天机器人，采用检索增强工作流整合四类信息源：专家编纂的共识指南、最新的PubMed文献、注册的干预性临床试验以及动态系统综述的证据。共识指南始终作为响应框架，其余来源由用户选择后并行检索。在针对50个临床问诊场景的探索性自动评估中，该聊天机器人在大模型评判下的平均评分与OpenEvidence相当，且数值更高、评分波动更小。
原文摘要 · Abstract (English)
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.
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