Advanced AI technologies are increasingly integrated into clinical domains to advance patient care. The design and development of clinical AI technologies necessitate seamless collaboration between clinical and technical experts. However, such interdisciplinary teams are often unsuccessful, with a lack of systematic analysis of collaboration barriers and coping strategies. This work examines two clinical AI collaborations in the context of speech-language pathology via semi-structured interviews with six clinical and seven technical experts. Using Activity Theory (AT) as our analytical lens, we examine persistent knowledge gaps and collaboration tensions across clinical and technical workflows, and show how clinical data can function as boundary objects while interdisciplinary collaborators may act as knowledge brokers to help address these challenges. Our findings contribute to CSCW research on interdisciplinary teams' data work by showing how shared clinical data, boundary objects, and broker roles shape coordination in early-stage clinical AI collaboration, and by providing insights into best practices for future collaboration.

翻译：高级人工智能技术正日益融入临床领域以提升患者护理水平。临床AI技术的设计与开发需要临床专家与技术专家的无缝协作。然而，此类跨学科团队往往难以成功，且缺乏对协作障碍与应对策略的系统性分析。本研究通过半结构化访谈（6位临床专家与7位技术专家），在言语病理学背景下考察了两项临床AI协作实践。以活动理论为分析框架，我们深入探究了临床与技术工作流中持续存在的知识鸿沟与协作张力，揭示了临床数据如何作为边界对象发挥作用，以及跨学科协作者如何扮演知识经纪人角色以应对这些挑战。本研究成果通过阐明共享临床数据、边界对象与经纪人角色如何塑造早期临床AI协作中的协调机制，并为未来协作提供最佳实践洞见，丰富了CSCW领域关于跨学科团队数据工作的研究。