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.

翻译：大型语言模型因其简单的基于文本的交互方式而在临床医生中广受欢迎，用于决策支持。然而，其对临床医生工作表现的影响尚不明确。由于不了解临床医生如何使用这项新技术，以及他们如何将其与传统临床决策支持系统进行比较，限制了设计能够克服现有工具局限性并提升性能和体验的新颖机制。这项定性研究探讨了临床医生（n=12）如何看待用于决策支持的不同交互模式（与大型语言模型的基于文本对话、交互式与静态用户界面以及语音交互）。在开放式使用基于大型语言模型的工具时，我们的参与者采取了一种以工具为中心的方法，使用简单的提示进行信息检索和确认，而非将其作为能够处理复杂问题的主动思考伙伴。交互设置的改变引发了批判性参与。参与度也因个体认知风格而异。最后，文本、语音和传统用户界面在临床决策支持中的交互优缺点表明，并不存在一种适用于所有情况的通用交互模式。