用大模型在问诊中自动生成证据导向问题，帮医生快速落地临床指南。
Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development
- 用零样本和多阶段推理策略生成问题，基于真实问诊录音
- 在80段脱敏病历上，生成问题与指南高度相关
- 适合临床辅助系统研发者，可减轻医生认知负担
循证医学（EBM）是高质量医疗的核心，但在快节奏的一线诊疗中难以实施。医生面临时间紧张、患者量大，且指南文档冗长，实时查阅不现实。为此，我们探索使用大语言模型（LLMs）作为环境助手，在医患对话中主动提出有针对性的循证问题。研究聚焦于问题生成而非回答，旨在辅助医生推理，并将指南实践融入短时问诊。采用Gemini 2.5作为主干模型，设计零样本基线与多阶段推理两种提示策略，在80段真实临床访谈脱敏数据上评估，由六位资深医师完成超90小时结构化评审。结果表明，尽管通用大模型尚不够可靠，但仍能生成具有临床意义且符合指南的问题，显示其显著潜力——可降低认知负荷，使循证医学更贴近临床实际场景。
原文摘要 · Abstract (English)
Evidence-based medicine (EBM) is central to high-quality care, but remains difficult to implement in fast-paced primary care settings. Physicians face short consultations, increasing patient loads, and lengthy guideline documents that are impractical to consult in real time. To address this gap, we investigate the feasibility of using large language models (LLMs) as ambient assistants that surface targeted, evidence-based questions during physician-patient encounters. Our study focuses on question generation rather than question answering, with the aim of scaffolding physician reasoning and integrating guideline-based practice into brief consultations. We implemented two prompting strategies, a zero-shot baseline and a multi-stage reasoning variant, using Gemini 2.5 as the backbone model. We evaluated on a benchmark of 80 de-identified transcripts from real clinical encounters, with six experienced physicians contributing over 90 hours of structured review. Results indicate that while general-purpose LLMs are not yet fully reliable, they can produce clinically meaningful and guideline-relevant questions, suggesting significant potential to reduce cognitive burden and make EBM more actionable at the point of care.
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