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Towards Better Health Conversations: The Benefits of Context-seeking

arXiv 2510.18880 ↗First posted 14 Sep 2025cs.HC cs.CL cs.CYcited by 0

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Abstract (arXiv)

Navigating health questions can be daunting in the modern information landscape. Large language models (LLMs) may provide tailored, accessible information, but also risk being inaccurate, biased or misleading. We present insights from 4 mixed-methods studies (total N=163), examining how people interact with LLMs for their own health questions. Qualitative studies revealed the importance of context-seeking in conversational AIs to elicit specific details a person may not volunteer or know to share. Context-seeking by LLMs was valued by participants, even if it meant deferring an answer for several turns. Incorporating these insights, we developed a "Wayfinding AI" to proactively solicit context. In a randomized, blinded study, participants rated the Wayfinding AI as more helpful, relevant, and tailored to their concerns compared to a baseline AI. These results demonstrate the strong impact of proactive context-seeking on conversational dynamics, and suggest design patterns for conversational AI to help navigate health topics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 95 references that are in the map. First posted on arXiv 14 Sep 2025.

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References · 19

  1. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  3. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
  4. 2025LLMs Get Lost In Multi-Turn Conversationcited by 429
  5. 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 429
  6. 2024MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoningcited by 154
  7. 2022Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluationcited by 142
  8. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  9. 2023A Critical Evaluation of Evaluations for Long-form Question Answeringcited by 111
  10. 2025MedHELM: Holistic Evaluation of Large Language Models for Medical Taskscited by 64
  11. 2024A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Modelscited by 45
  12. 2023Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AIcited by 32
7 more
  1. 2025ALFA: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoningcited by 25
  2. 2025Advancing Conversational Diagnostic AI with Multimodal Reasoningcited by 20
  3. 2024Personalizing Prostate Cancer Education for Patients Using an EHR-Integrated LLM Agentcited by 7
  4. 2025Clinical knowledge in LLMs does not translate to human interactionscited by 7
  5. 2024Are Human Conversations Special? A Large Language Model Perspectivecited by 5
  6. 2022The Impact of Explanations on Layperson Trust in Artificial Intelligence-Driven Symptom Checker Apps: Experimental Studycited by 2
  7. 2024The Odyssey Journey: Top-Tier Medical Resource Seeking for Specialized Disorder in Chinacited by 1

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