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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.23937 |
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| _version_ | 1866908912457875456 |
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| author | Ji, Zongliang Zhang, Ziyang Tan, Xincheng Thompson, Matthew Goldenberg, Anna Yang, Carl Krishnan, Rahul G. Zhang, Fan |
| author_facet | Ji, Zongliang Zhang, Ziyang Tan, Xincheng Thompson, Matthew Goldenberg, Anna Yang, Carl Krishnan, Rahul G. Zhang, Fan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23937 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development Ji, Zongliang Zhang, Ziyang Tan, Xincheng Thompson, Matthew Goldenberg, Anna Yang, Carl Krishnan, Rahul G. Zhang, Fan Computation and Language Machine Learning 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. |
| title | Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2603.23937 |