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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

Japanese

人工知能は、特に、矛盾する情報と共性病気が不確実性と実践の変異を引き起こす複雑なケースで、血動学的決定をサポートすることができる。しかし、これらのシステムの現実世界の価値は、正確性だけでなく、どのようにクリニックの決定の質に影響を与えるかによって死んでいます。私たちは、最初に6988人のセプシス患者でAIシステムの勧告を再検討し、彼らがオリジナルのプロバイダーの決定を全体的に改善したが、複雑なケースで大幅に劣るパフォーマンスを持っていたことを発見しました。それから、私たちは228人のアメリカの重要なケア医師と事前に登録された実験を行い、6つの複雑な治療の決定でこの不完全なアドバイスを使用してテストしました。彼ら

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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