研究医生如何用不同方式与AI互动，发现文本、语音和界面各有优劣。
From Retrieving Information to Reasoning with AI: Exploring Different Interaction Modalities to Support Human-AI Coordination in Clinical Decision-Making
- 医生更倾向用AI查资料而非深度讨论，多用简单提问。
- 改变交互方式能提升医生参与度，效果因人而异。
- 无通用最佳交互方式，需适配个体认知风格。
大型语言模型（LLM）因其简单的文本交互方式，被临床医生广泛用于决策支持，但其对医生表现的实际影响尚不明确。由于缺乏对医生如何使用该技术及其与传统临床决策支持系统（CDSS）对比的了解，限制了新型机制的设计，难以突破现有工具局限并提升性能与体验。本项定性研究考察了12名临床医生在使用基于LLM的工具时，对不同交互模态（基于文本的对话、交互式/静态用户界面、语音）的感知。在开放式使用中，参与者倾向于采用以工具为中心的方法，通过简单提示进行信息检索和确认，而非将其作为处理复杂问题的主动协商伙伴。当交互设置发生变化时，关键参与度才得以显现；同时，参与度也受个体认知风格影响。此外，文本、语音及传统界面在临床决策支持中的优缺点表明，不存在适用于所有场景的单一交互模态。
原文摘要 · Abstract (English)
LLMs are popular among clinicians for decision-support because of simple text-based interaction. However, their impact on clinicians' performance is ambiguous. Not knowing how clinicians use this new technology and how they compare it to traditional clinical decision-support systems (CDSS) restricts designing novel mechanisms that overcome existing tool limitations and enhance performance and experience. This qualitative study examines how clinicians (n=12) perceive different interaction modalities (text-based conversation with LLMs, interactive and static UI, and voice) for decision-support. In open-ended use of LLM-based tools, our participants took a tool-centric approach using them for information retrieval and confirmation with simple prompts instead of use as active deliberation partners that can handle complex questions. Critical engagement emerged with changes to the interaction setup. Engagement also differed with individual cognitive styles. Lastly, benefits and drawbacks of interaction with text, voice and traditional UIs for clinical decision-support show the lack of a one-size-fits-all interaction modality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。