Natural Language Processing (NLP) techniques have been increasingly integrated into clinical projects to advance clinical decision-making and improve patient outcomes. Such projects benefit from interdisciplinary team collaborations. This paper explores challenges and opportunities using two clinical NLP projects as case studies, where speech-language pathologists (SLPs) and NLP researchers jointly developed technology-based systems to improve clinical workflow. Through semi-structured interviews with five SLPs and four NLP researchers, we collected collaboration practices and challenges. Using Activity Theory as an analytical framework, we examined collaborative activities, challenges, and strategies to bridge interdisciplinary gaps. Our findings revealed significant knowledge boundaries and terminological barriers between SLPs and NLP researchers when both groups relied on clinical data as boundary objects to facilitate collaboration, although this approach has limitations. We highlight the potential opportunities of AI technologies as knowledge brokers to overcome interdisciplinary collaboration challenges.

翻译：自然语言处理技术正日益融入临床项目，以推动临床决策制定并改善患者预后。此类项目得益于跨学科团队协作。本文通过两个临床自然语言处理项目作为案例研究，探讨了其中的挑战与机遇。在这些案例中，言语语言病理学家与自然语言处理研究人员共同开发了基于技术的系统以优化临床工作流程。通过对五位言语语言病理学家和四位自然语言处理研究人员的半结构化访谈，我们收集了协作实践与挑战。运用活动理论作为分析框架，我们考察了协作活动、挑战以及弥合跨学科鸿沟的策略。研究发现，当言语语言病理学家与自然语言处理研究人员依赖临床数据作为边界对象促进协作时，两组人员之间存在显著的知识边界与术语障碍，尽管该方法存在局限性。我们重点探讨了人工智能技术作为知识中介在克服跨学科协作挑战方面的潜在机遇。