为重症监护AI决策系统设计提供临床需求依据
Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care
- 通过访谈7名重症医护，提炼出决策支持系统的核心需求
- 发现患者状态复杂性与多因素决策是临床核心挑战
- 提出可提升信任度的AI系统设计建议，适合医疗AI研发者参考
随着人工智能模型日益复杂且难以解释，数字系统在临床决策中的支持作用愈发重要。这种复杂性引发对可信度的担忧，影响技术的安全有效应用。尤其在数据密集、节奏快速的重症监护室（ICU）环境中，理解决策过程及解释需求至关重要。本研究通过对7名来自不同岗位和经验水平的ICU医护人员进行小组访谈，采用主题分析法识别出三个核心主题：（T1）ICU决策依赖广泛因素；（T2）患者状态复杂性给共同决策带来挑战；（T3）AI决策支持系统的需求与能力。基于临床反馈，提出具体设计建议，为未来重症监护领域AI系统开发提供指导。
原文摘要 · Abstract (English)
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustworthiness, impacting safe and effective adoption of such technologies. Improved understanding of decision-making processes and requirements for explanations coming from decision support tools is a vital component in providing effective explainable solutions. This is particularly relevant in the data-intensive, fast-paced environments of intensive care units (ICUs). To explore these issues, group interviews were conducted with seven ICU clinicians, representing various roles and experience levels. Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。