Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.

翻译：人工智能（AI）的进步带来了前所未有的能力，但创新团队在构想AI概念时却面临困境。数据科学团队构思的是用户不需要的创新，而领域专家设想的却是无法构建的创新。缺乏有效的构思似乎是一个关键衔接点。跨学科团队如何才能识别出既可行又可取的用例？本文以第一人称视角，呈现了为改善重症医学而构思AI概念的实践过程。作为由数据科学家、临床医生和人机交互研究者组成的团队，我们开展了一系列设计工作坊，以探索更有效的AI概念构思与问题构建方法。我们详细阐述了工作流程、遇到的挑战，以及被证明有效的实践与产物。我们还探讨了改进协作与利益相关方参与的研究启示，并剖析了人机交互在降低AI创新高失败率方面可能发挥的作用。