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Indirect Influences of Artificial Intelligence Advice on Complex Sepsis Treatment Decisions

Venkatesh Sivaraman, Joel Levin, Eric P. Mason, Victor Talisa, Suchi Saria, Jean Feng, Andrew J. King, Jeremy M. Kahn, Adam Perer · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24313 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

间接性分裂症是一个主要的公共卫生问题,许多分裂症患者死于无效的血动态管理。人工智能可以支持血动态决策,特别是在复杂的案例中,其中矛盾的信息和伴随性疾病驱动的不确定性和实践变化。然而,这些系统的真实价值取决于不仅仅是准确性,而是它们如何影响临床医生决策的质量。我们首先反复评估了AI系统的建议,在6928个分裂症患者中,发现它们改善了原始提供者的决定总体上,但在复杂的案例中表现更差。然后我们与228名美国临床护理医生进行预注册实验,以测试他们在六个复杂的治疗决策中使用这种不完美的建议,使用三种类型的建议,旨在改变协作自律性。参与者给予简单的建议

Machine translation (Meta M2M100); the English original follows.

Abstract

Sepsis is a major public health concern, and many sepsis patients die due to ineffective hemodynamic management. Artificial intelligence could support hemodynamic decisions, particularly in complex cases where conflicting information and comorbidities drive uncertainty and practice variation. However, these systems' real-world value depends on not just accuracy, but how they influence the quality of clinician decisions. We first retrospectively evaluated an AI system's recommendations in 6,928 sepsis patients, finding that they improved upon the original providers' decisions overall but had significantly worse performance in complex cases. We then conducted a pre-registered experiment with 228 US critical care physicians to test their use of this imperfect advice in six complex treatment decisions, using one of three advice types designed to vary reasoning autonomy. Participants given simple model recommendations overcame poor advice, choosing optimal treatments in 56.3% of decisions compared to 31.8% if they had always followed the recommendations. They also made significantly better decisions than those receiving a less prescriptive advice type (42.9%). Physicians selectively adapted and improved weak advice, likely because it prompted them to reconsider their assessment. Our findings suggest that improving sepsis care decisions with AI may require accounting for these indirect influences resulting from human-AI collaborative reasoning.

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Topics: Advice (programming), Medicine, Sepsis, Intensive care medicine, Imperfect, Quality (philosophy)

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