Developing artificial intelligence (AI) tools for healthcare is a multiple disciplinary effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the AI development workflow and how participants navigate the challenges and tensions of sharing and generating knowledge across disciplines. Through an inductive thematic analysis of 13 semi-structured interviews with participants in a large research consortia, our findings suggest that multiple disciplinarity heavily impacts work practices. Participants faced challenges to learn the languages of other disciplines and needed to adapt the tools used for sharing and communicating with their audience, particularly those from a clinical or patient perspective. Large health datasets also posed certain restrictions on work practices. We identified meetings as a key platform for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge. Finally, we discuss design implications for data science and collaborative tools, and recommendations for future research.

翻译：开发医疗人工智能工具是一项多学科协作的工作，涉及数据科学家、临床医生、患者及其他学科领域人员。本文探讨了人工智能开发流程，以及参与者如何在跨学科知识共享与生成中应对挑战与矛盾。通过对大型研究联盟参与者的13次半结构化访谈进行归纳式主题分析，我们发现多学科性对工作实践产生显著影响。参与者面临学习其他学科语言、调整共享与沟通工具以适应受众（尤其是临床或患者视角）的挑战。大型健康数据集也对工作实践施加了特定限制。会议被确立为促进学科间交流、实现知识融合与创新的关键平台。最后，我们讨论了数据科学与协作工具的设计启示，并提出了未来研究建议。