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.

该研究旨在探讨AI系统的设计要求，以支持临床专家在早期诊断败血症方面做出更好的决策。通过对现有AI预测模块的基于医疗记录的电子健康档案系统的临床专家的形态学研究，该研究构建了SepsisLab，通过AI算法预测败血症发展并可视化预测的不确定性，并提供减少不确定性的可行建议，从而展示了SepsisLab在人机协作方面的潜力，对未来的AI辅助败血症诊断和其他高风险医学决策具有重要意义。

复杂医学决策中人工智能人机协同的再思考：一项败血症诊断案例研究