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
La sepsis est une préoccupation majeure de la santé publique, et de nombreux patients souffrant de sepsis meurent en raison d'une gestion hémodynamique inefficace. L'intelligence artificielle pourrait soutenir les décisions hémodynamiques, en particulier dans les cas complexes où l'information conflictuelle et les comorbidités entraînent l'incertitude et la variation de la pratique. Cependant, la valeur réelle de ces systèmes dépend non seulement de la précision, mais de la façon dont ils influencent la qualité des décisions cliniques. Nous avons d'abord évalué rétrospectivement les recommandations d'un système d'IA chez 6.928 patients souffrant de sepsis, constatant qu'elles ont amélioré les décisions des fournisseurs originaux dans
Machine translation (Meta M2M100); the English original follows.
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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