Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose
into the stomach to deliver nutrition or medication. If not placed correctly,
they can cause serious harm, even death to patients. Recent AI developments
demonstrate the feasibility of robustly detecting NGT placement from Chest
X-ray images to reduce risks of sub-optimally or critically placed NGTs being
missed or delayed in their detection, but gaps remain in clinical practice
integration. In this study, we present a human-centered approach to the problem
and describe insights derived following contextual inquiry and in-depth
interviews with 15 clinical stakeholders. The interviews helped understand
challenges in existing workflows, and how best to align technical capabilities
with user needs and expectations. We discovered the trade-offs and complexities
that need consideration when choosing suitable workflow stages, target users,
and design configurations for different AI proposals. We explored how to
balance AI benefits and risks for healthcare staff and patients within broader
organizational and medical-legal constraints. We also identified data issues
related to edge cases and data biases that affect model training and
evaluation; how data documentation practices influence data preparation and
labelling; and how to measure relevant AI outcomes reliably in future
evaluations. We discuss how our work informs design and development of AI
applications that are clinically useful, ethical, and acceptable in real-world
healthcare services.

鼻胃管插管是通过鼻子插入胃部，用于输送营养或药物的插管。最近的人工智能发展展示了通过胸部 X 射线图像可可靠地检测鼻胃管的放置，以减少次优地或临界地放置的鼻胃管被遗漏或延迟检测的风险，但在临床实践整合方面仍存在差距。通过本研究，我们提出了一种以人为中心的方法来解决这个问题，并描述了在与 15 名临床利益相关者进行情境调查和深入访谈后所得到的洞察。访谈帮助我们了解现有工作流程中的挑战，以及如何在技术能力与用户需求和期望之间达到最佳对齐。我们发现在为不同的人工智能提案选择适当的工作流程阶段、目标用户和设计配置时需要考虑的权衡和复杂性。我们探讨了如何在更广泛的组织和医疗法律约束条件下平衡医疗人员和患者的人工智能益处和风险。我们还发现与边缘情况和数据偏差相关的数据问题影响了模型训练和评估；数据文档实践如何影响数据准备和标注；以及如何可靠地衡量未来评估中的相关人工智能成果。我们讨论了我们的工作如何为临床上有用、符合伦理和可接受的人工智能应用的设计和开发提供指导。