Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

翻译：当下用于医疗决策支持的AI系统在科研论文的标准数据集上常常表现出色，但在实际部署中却屡屡失败。本研究聚焦脓毒症的决策过程。脓毒症是一种危及生命的急性全身性感染，需要临床医生在高度不确定性下进行早期诊断。我们的目标是为支持临床专家在脓毒症早期诊断中做出更优决策的AI系统探索设计需求。研究首先开展了一项形成性研究，探究临床专家为何弃用其电子健康记录（EHR）系统中已有的脓毒症预测智能模块。我们认为，以人为中心的AI系统需要支持人类专家在医疗决策过程的中期阶段（如提出假设或收集数据），而非仅关注最终决策。因此，我们基于当前最先进的AI算法构建了SepsisLab系统，并将其扩展以预测脓毒症的未来发展轨迹、可视化预测不确定性，并提出可操作的建议（即应补充哪些实验室检测项目）以降低这种不确定性。通过六位临床医生使用我们原型系统的启发式评估，我们证明SepsisLab为未来AI辅助的脓毒症诊断及其他高风险医疗决策构建了一种富有前景的人机协作范式。