Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-making focus on calibrating reliance on AI advice, leaving it unclear how different system designs might influence the reasoning processes underneath. We address this gap by reconsidering AI interfaces as collections of intelligent reasoning cues: discrete pieces of AI information that can individually influence decision-making. We then explore the roles of eight types of reasoning cues in a high-stakes clinical decision (treating patients with sepsis in intensive care). Through contextual inquiries with six teams and a think-aloud study with 25 physicians, we find that reasoning cues have distinct patterns of influence that can directly inform design. Our results also suggest that reasoning cues should prioritize tasks with high variability and discretion, adapt to ensure compatibility with evolving decision needs, and provide complementary, rigorous insights on complex cases.

翻译：基于人工智能的决策支持系统虽能实现高精度，却仍可能无法有效辅助用户或改善决策。现有AI辅助决策理论主要关注对AI建议的依赖度校准，而未能阐明不同系统设计如何影响底层的推理过程。为填补这一空白，本研究将AI界面重新构想为智能推理线索的集合：这些离散的AI信息单元能够独立影响决策过程。我们通过一个高风险临床决策案例（重症监护室脓毒症患者治疗），深入探究了八类推理线索的作用机制。通过对六个医疗团队的场景化访谈及25位医师的出声思维实验，我们发现推理线索具有独特的影响模式，可直接为系统设计提供依据。研究结果进一步表明：推理线索应优先应用于高变异性和高自主判断需求的任务，需具备适应性以确保与动态决策需求的兼容性，并为复杂病例提供互补且严谨的决策洞见。