[2603.23937] Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development

Computer Science > Computation and Language arXiv:2603.23937 (cs) [Submitted on 25 Mar 2026] Title:Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development Authors:Zongliang Ji, Ziyang Zhang, Xincheng Tan, Matthew Thompson, Anna Goldenberg, Carl Yang, Rahul G. Krishnan, Fan Zhang View a PDF of the paper titled Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development, by Zongliang Ji and Ziyang Zhang and Xincheng Tan and Matthew Thompson and Anna Goldenberg and Carl Yang and Rahul G. Krishnan and Fan Zhang View PDF HTML (experimental) Abstract: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 encounter...